Revise important notes on third-party system usage

Updated important notes regarding third-party systems and responsibilities when using the Microsoft Agent Framework.
Python: [BREAKING] Remove deprecated Python OpenAI/Azure AI surfaces (#4990 )
2026-06-16 21:04:09 +08:00 · 2026-03-31 14:46:08 -07:00 · 2026-03-31 20:36:21 +00:00 · 2026-03-31 20:00:40 +00:00 · 2026-03-31 19:40:08 +00:00 · 2026-03-31 18:44:27 +00:00
1057 changed files with 45217 additions and 40353 deletions
@@ -24,7 +24,9 @@ runs:
  using: "composite"
  steps:
    - name: Set up Node.js environment
-      uses: actions/setup-node@v4
+      uses: actions/setup-node@v6
+      with:
+        node-version: 22

    - name: Install Copilot CLI
      shell: bash
@@ -32,7 +34,7 @@ runs:

    - name: Test Copilot CLI
      shell: bash
-      run: copilot -p "What can you do in one sentence?"
+      run: copilot --version && copilot -p "What can you do in one sentence?"

    - name: Azure CLI Login
      uses: azure/login@v2
@@ -0,0 +1,166 @@
+name: Setup Local MCP Server
+description: Start and validate a local streamable HTTP MCP server for integration tests
+
+inputs:
+  fallback_url:
+    description: Existing LOCAL_MCP_URL value to keep as a fallback if local startup fails
+    required: false
+    default: ''
+  host:
+    description: Host interface to bind the local MCP server
+    required: false
+    default: '127.0.0.1'
+  port:
+    description: Port to bind the local MCP server
+    required: false
+    default: '8011'
+  mount_path:
+    description: Mount path for the local streamable HTTP MCP endpoint
+    required: false
+    default: '/mcp'
+
+outputs:
+  effective_url:
+    description: Local MCP URL when startup succeeds, otherwise the provided fallback URL
+    value: ${{ steps.start.outputs.effective_url }}
+  local_url:
+    description: URL of the local MCP server
+    value: ${{ steps.start.outputs.local_url }}
+  started:
+    description: Whether the local MCP server started and passed validation
+    value: ${{ steps.start.outputs.started }}
+  pid:
+    description: PID of the local MCP server process when startup succeeded
+    value: ${{ steps.start.outputs.pid }}
+
+runs:
+  using: composite
+  steps:
+    - name: Start and validate local MCP server
+      id: start
+      shell: bash
+      run: |
+        set -euo pipefail
+
+        host="${{ inputs.host }}"
+        port="${{ inputs.port }}"
+        mount_path="${{ inputs.mount_path }}"
+        fallback_url="${{ inputs.fallback_url }}"
+
+        if [[ ! "$mount_path" =~ ^/ ]]; then
+          mount_path="/$mount_path"
+        fi
+
+        local_url="http://${host}:${port}${mount_path}"
+        health_url="http://${host}:${port}/healthz"
+        log_file="$RUNNER_TEMP/local-mcp-server.log"
+        pid_file="$RUNNER_TEMP/local-mcp-server.pid"
+        rm -f "$log_file" "$pid_file"
+
+        server_pid="$(
+          python3 - "$GITHUB_WORKSPACE/python" "$log_file" "$host" "$port" "$mount_path" <<'PY'
+        from __future__ import annotations
+
+        import subprocess
+        import sys
+
+        workspace, log_file, host, port, mount_path = sys.argv[1:]
+
+        with open(log_file, "w", encoding="utf-8") as log:
+            process = subprocess.Popen(
+                [
+                    "uv",
+                    "run",
+                    "python",
+                    "scripts/local_mcp_streamable_http_server.py",
+                    "--host",
+                    host,
+                    "--port",
+                    port,
+                    "--mount-path",
+                    mount_path,
+                ],
+                cwd=workspace,
+                stdout=log,
+                stderr=subprocess.STDOUT,
+                start_new_session=True,
+            )
+
+        print(process.pid)
+        PY
+        )"
+        echo "$server_pid" > "$pid_file"
+
+        started=false
+        for _ in $(seq 1 30); do
+          if curl --silent --fail "$health_url" >/dev/null; then
+            started=true
+            break
+          fi
+          if ! kill -0 "$server_pid" 2>/dev/null; then
+            break
+          fi
+          sleep 1
+        done
+
+        if [[ "$started" == "true" ]]; then
+          if ! (
+            cd "$GITHUB_WORKSPACE/python"
+            LOCAL_MCP_URL="$local_url" uv run python - <<'PY'
+        from __future__ import annotations
+
+        import asyncio
+        import os
+
+        from agent_framework import Content, MCPStreamableHTTPTool
+
+
+        def result_to_text(result: str | list[Content]) -> str:
+            if isinstance(result, str):
+                return result
+            return "\n".join(content.text for content in result if content.type == "text" and content.text)
+
+
+        async def main() -> None:
+            tool = MCPStreamableHTTPTool(
+                name="local_ci_mcp",
+                url=os.environ["LOCAL_MCP_URL"],
+                approval_mode="never_require",
+            )
+
+            async with tool:
+                assert tool.functions, "Local MCP server did not expose any tools."
+                result = result_to_text(await tool.functions[0].invoke(query="What is Agent Framework?"))
+                assert result, "Local MCP server returned an empty response."
+
+
+        asyncio.run(main())
+        PY
+          ); then
+            started=false
+          fi
+        fi
+
+        effective_url="$local_url"
+        pid="$server_pid"
+
+        if [[ "$started" != "true" ]]; then
+          effective_url="$fallback_url"
+          pid=""
+          if kill -0 "$server_pid" 2>/dev/null; then
+            kill -TERM -- "-$server_pid" 2>/dev/null || kill -TERM "$server_pid" || true
+            sleep 1
+            kill -KILL -- "-$server_pid" 2>/dev/null || kill -KILL "$server_pid" || true
+          fi
+          echo "Local MCP server was unavailable; continuing with fallback LOCAL_MCP_URL."
+          if [[ -f "$log_file" ]]; then
+            tail -n 100 "$log_file" || true
+          fi
+        else
+          echo "Using local MCP server at $local_url"
+        fi
+
+        echo "started=$started" >> "$GITHUB_OUTPUT"
+        echo "local_url=$local_url" >> "$GITHUB_OUTPUT"
+        echo "effective_url=$effective_url" >> "$GITHUB_OUTPUT"
+        echo "pid=$pid" >> "$GITHUB_OUTPUT"
@@ -41,8 +41,7 @@ ENFORCED_TARGETS: set[str] = {
    "packages.purview.agent_framework_purview",
    "packages.anthropic.agent_framework_anthropic",
    "packages.azure-ai-search.agent_framework_azure_ai_search",
-    "packages.core.agent_framework.azure",
-    "packages.core.agent_framework.openai",
+    "packages.openai.agent_framework_openai",
    # Individual files (if you want to enforce specific files instead of whole packages)
    "packages/core/agent_framework/observability.py",
    # Add more targets here as coverage improves
@@ -60,9 +60,10 @@ jobs:
    environment: integration
    timeout-minutes: 60
    env:
-      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_EMBEDDINGS_MODEL_ID: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
+      OPENAI_CHAT_MODEL: ${{ vars.OPENAI__CHATMODELID }}
+      OPENAI_RESPONSES_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
+      OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
+      OPENAI_EMBEDDING_MODEL: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
    defaults:
      run:
@@ -81,8 +82,8 @@ jobs:
      - name: Test with pytest (OpenAI integration)
        run: >
          uv run pytest --import-mode=importlib
-          packages/core/tests/openai
-          -m integration
+          packages/openai/tests
+          -m "integration and not azure"
          -n logical --dist worksteal
          --timeout=120 --session-timeout=900 --timeout_method thread
          --retries 2 --retry-delay 5
@@ -96,7 +97,8 @@ jobs:
    env:
      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
+      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME }}
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
    defaults:
      run:
@@ -121,7 +123,9 @@ jobs:
      - name: Test with pytest (Azure OpenAI integration)
        run: >
          uv run pytest --import-mode=importlib
-          packages/core/tests/azure
+          packages/openai/tests/openai/test_openai_chat_completion_client_azure.py
+          packages/openai/tests/openai/test_openai_chat_client_azure.py
+          packages/openai/tests/openai/test_openai_embedding_client_azure.py
          -m integration
          -n logical --dist worksteal
          --timeout=120 --session-timeout=900 --timeout_method thread
@@ -151,6 +155,13 @@ jobs:
        with:
          python-version: ${{ env.UV_PYTHON }}
          os: ${{ runner.os }}
+      - name: Start local MCP server
+        id: local-mcp
+        uses: ./.github/actions/setup-local-mcp-server
+        with:
+          fallback_url: ${{ env.LOCAL_MCP_URL }}
+      - name: Prefer local MCP URL when available
+        run: echo "LOCAL_MCP_URL=${{ steps.local-mcp.outputs.effective_url }}" >> "$GITHUB_ENV"
      - name: Test with pytest (Anthropic, Ollama, MCP integration)
        run: >
          uv run pytest --import-mode=importlib
@@ -161,6 +172,26 @@ jobs:
          -n logical --dist worksteal
          --timeout=120 --session-timeout=900 --timeout_method thread
          --retries 2 --retry-delay 5
+      - name: Stop local MCP server
+        if: always()
+        shell: bash
+        run: |
+          set -euo pipefail
+          server_pid="${{ steps.local-mcp.outputs.pid }}"
+          if [[ -z "$server_pid" ]]; then
+            exit 0
+          fi
+          if ! kill -0 "$server_pid" 2>/dev/null; then
+            exit 0
+          fi
+          kill -TERM -- "-$server_pid" 2>/dev/null || kill -TERM "$server_pid" 2>/dev/null || true
+          for _ in $(seq 1 10); do
+            if ! kill -0 "$server_pid" 2>/dev/null; then
+              exit 0
+            fi
+            sleep 1
+          done
+          kill -KILL -- "-$server_pid" 2>/dev/null || kill -KILL "$server_pid" 2>/dev/null || true

  # Azure Functions + Durable Task integration tests
  python-tests-functions:
@@ -170,12 +201,16 @@ jobs:
    timeout-minutes: 60
    env:
      UV_PYTHON: "3.11"
-      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
+      OPENAI_CHAT_MODEL: ${{ vars.OPENAI__CHATMODELID }}
+      OPENAI_RESPONSES_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
+      OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
+      OPENAI_EMBEDDING_MODEL: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
+      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
+      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
+      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
      FUNCTIONS_WORKER_RUNTIME: "python"
      DURABLE_TASK_SCHEDULER_CONNECTION_STRING: "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None"
      AzureWebJobsStorage: "UseDevelopmentStorage=true"
@@ -209,18 +244,23 @@ jobs:
          packages/durabletask/tests/integration_tests
          -m integration
          -n logical --dist worksteal
-          --timeout=120 --session-timeout=900 --timeout_method thread
+          -x
+          --timeout=360 --session-timeout=900 --timeout_method thread
          --retries 2 --retry-delay 5

-  # Azure AI integration tests
-  python-tests-azure-ai:
-    name: Python Integration Tests - Azure AI
+  # Foundry integration tests
+  python-tests-foundry:
+    name: Python Integration Tests - Foundry
    runs-on: ubuntu-latest
    environment: integration
    timeout-minutes: 60
    env:
      AZURE_AI_PROJECT_ENDPOINT: ${{ secrets.AZUREAI__ENDPOINT }}
      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREAI__DEPLOYMENTNAME }}
+      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
+      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
+      FOUNDRY_AGENT_NAME: ${{ vars.FOUNDRY_AGENT_NAME }}
+      FOUNDRY_AGENT_VERSION: ${{ vars.FOUNDRY_AGENT_VERSION }}
      LOCAL_MCP_URL: ${{ vars.LOCAL_MCP__URL }}
    defaults:
      run:
@@ -244,7 +284,13 @@ jobs:
          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
      - name: Test with pytest
        timeout-minutes: 15
-        run: uv run --directory packages/azure-ai poe integration-tests -n logical --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5
+        run: >
+          uv run pytest --import-mode=importlib
+          packages/foundry/tests
+          -m integration
+          -n logical --dist worksteal
+          --timeout=120 --session-timeout=900 --timeout_method thread
+          --retries 2 --retry-delay 5

  # Azure Cosmos integration tests
  python-tests-cosmos:
@@ -301,7 +347,7 @@ jobs:
        python-tests-azure-openai,
        python-tests-misc-integration,
        python-tests-functions,
-        python-tests-azure-ai,
+        python-tests-foundry,
        python-tests-cosmos
      ]
    steps:
@@ -47,6 +47,9 @@ jobs:
          filters: |
            python:
              - 'python/**'
+              - '.github/actions/setup-local-mcp-server/**'
+              - '.github/workflows/python-merge-tests.yml'
+              - '.github/workflows/python-integration-tests.yml'
            core:
              - 'python/packages/core/agent_framework/_*.py'
              - 'python/packages/core/agent_framework/_workflows/**'
@@ -54,20 +57,28 @@ jobs:
              - 'python/packages/core/agent_framework/observability.py'
            openai:
              - 'python/packages/core/agent_framework/openai/**'
-              - 'python/packages/core/tests/openai/**'
+              - 'python/packages/openai/**'
+              - 'python/samples/**/providers/openai/**'
            azure:
+              - 'python/packages/openai/**'
              - 'python/packages/core/agent_framework/azure/**'
-              - 'python/packages/core/tests/azure/**'
+              - 'python/samples/**/providers/azure/**'
            misc:
              - 'python/packages/anthropic/**'
              - 'python/packages/ollama/**'
              - 'python/packages/core/agent_framework/_mcp.py'
              - 'python/packages/core/tests/core/test_mcp.py'
+              - 'python/scripts/local_mcp_streamable_http_server.py'
+              - '.github/actions/setup-local-mcp-server/**'
+              - '.github/workflows/python-merge-tests.yml'
+              - '.github/workflows/python-integration-tests.yml'
            functions:
              - 'python/packages/azurefunctions/**'
              - 'python/packages/durabletask/**'
            azure-ai:
              - 'python/packages/azure-ai/**'
+              - 'python/packages/foundry/**'
+              - 'python/samples/**/providers/foundry/**'
            cosmos:
              - 'python/packages/azure-cosmos/**'
      # run only if 'python' files were changed
@@ -128,9 +139,10 @@ jobs:
    runs-on: ubuntu-latest
    environment: integration
    env:
-      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
-      OPENAI_EMBEDDINGS_MODEL_ID: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
+      OPENAI_CHAT_MODEL: ${{ vars.OPENAI__CHATMODELID }}
+      OPENAI_RESPONSES_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
+      OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
+      OPENAI_EMBEDDING_MODEL: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
    defaults:
      run:
@@ -146,8 +158,8 @@ jobs:
      - name: Test with pytest (OpenAI integration)
        run: >
          uv run pytest --import-mode=importlib
-          packages/core/tests/openai
-          -m integration
+          packages/openai/tests
+          -m "integration and not azure"
          -n logical --dist worksteal
          --timeout=120 --session-timeout=900 --timeout_method thread
          --retries 2 --retry-delay 5
@@ -182,7 +194,8 @@ jobs:
    env:
      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
+      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME }}
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
    defaults:
      run:
@@ -205,7 +218,9 @@ jobs:
      - name: Test with pytest (Azure OpenAI integration)
        run: >
          uv run pytest --import-mode=importlib
-          packages/core/tests/azure
+          packages/openai/tests/openai/test_openai_chat_completion_client_azure.py
+          packages/openai/tests/openai/test_openai_chat_client_azure.py
+          packages/openai/tests/openai/test_openai_embedding_client_azure.py
          -m integration
          -n logical --dist worksteal
          --timeout=120 --session-timeout=900 --timeout_method thread
@@ -253,6 +268,13 @@ jobs:
        with:
          python-version: ${{ env.UV_PYTHON }}
          os: ${{ runner.os }}
+      - name: Start local MCP server
+        id: local-mcp
+        uses: ./.github/actions/setup-local-mcp-server
+        with:
+          fallback_url: ${{ env.LOCAL_MCP_URL }}
+      - name: Prefer local MCP URL when available
+        run: echo "LOCAL_MCP_URL=${{ steps.local-mcp.outputs.effective_url }}" >> "$GITHUB_ENV"
      - name: Test with pytest (Anthropic, Ollama, MCP integration)
        run: >
          uv run pytest --import-mode=importlib
@@ -264,6 +286,26 @@ jobs:
          --timeout=120 --session-timeout=900 --timeout_method thread
          --retries 2 --retry-delay 5
        working-directory: ./python
+      - name: Stop local MCP server
+        if: always()
+        shell: bash
+        run: |
+          set -euo pipefail
+          server_pid="${{ steps.local-mcp.outputs.pid }}"
+          if [[ -z "$server_pid" ]]; then
+            exit 0
+          fi
+          if ! kill -0 "$server_pid" 2>/dev/null; then
+            exit 0
+          fi
+          kill -TERM -- "-$server_pid" 2>/dev/null || kill -TERM "$server_pid" 2>/dev/null || true
+          for _ in $(seq 1 10); do
+            if ! kill -0 "$server_pid" 2>/dev/null; then
+              exit 0
+            fi
+            sleep 1
+          done
+          kill -KILL -- "-$server_pid" 2>/dev/null || kill -KILL "$server_pid" 2>/dev/null || true
      - name: Surface failing tests
        if: always()
        uses: pmeier/pytest-results-action@v0.7.2
@@ -288,12 +330,16 @@ jobs:
    environment: integration
    env:
      UV_PYTHON: "3.11"
-      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
-      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
+      OPENAI_CHAT_MODEL: ${{ vars.OPENAI__CHATMODELID }}
+      OPENAI_RESPONSES_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
+      OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
+      OPENAI_EMBEDDING_MODEL: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
+      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
+      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
+      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
      FUNCTIONS_WORKER_RUNTIME: "python"
      DURABLE_TASK_SCHEDULER_CONNECTION_STRING: "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None"
      AzureWebJobsStorage: "UseDevelopmentStorage=true"
@@ -325,7 +371,8 @@ jobs:
          packages/durabletask/tests/integration_tests
          -m integration
          -n logical --dist worksteal
-          --timeout=120 --session-timeout=900 --timeout_method thread
+          -x
+          --timeout=360 --session-timeout=900 --timeout_method thread
          --retries 2 --retry-delay 5
        working-directory: ./python
      - name: Surface failing tests
@@ -338,8 +385,8 @@ jobs:
          fail-on-empty: false
          title: Functions integration test results

-  python-tests-azure-ai:
-    name: Python Tests - Azure AI
+  python-tests-foundry:
+    name: Python Integration Tests - Foundry
    needs: paths-filter
    if: >
      github.event_name != 'pull_request' &&
@@ -352,6 +399,10 @@ jobs:
    env:
      AZURE_AI_PROJECT_ENDPOINT: ${{ secrets.AZUREAI__ENDPOINT }}
      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREAI__DEPLOYMENTNAME }}
+      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
+      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
+      FOUNDRY_AGENT_NAME: ${{ vars.FOUNDRY_AGENT_NAME }}
+      FOUNDRY_AGENT_VERSION: ${{ vars.FOUNDRY_AGENT_VERSION }}
      LOCAL_MCP_URL: ${{ vars.LOCAL_MCP__URL }}
    defaults:
      run:
@@ -373,7 +424,13 @@ jobs:
          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
      - name: Test with pytest
        timeout-minutes: 15
-        run: uv run --directory packages/azure-ai poe integration-tests -n logical --dist worksteal --timeout=120 --session-timeout=900 --timeout_method thread --retries 2 --retry-delay 5
+        run: >
+          uv run pytest --import-mode=importlib
+          packages/foundry/tests
+          -m integration
+          -n logical --dist worksteal
+          --timeout=120 --session-timeout=900 --timeout_method thread
+          --retries 2 --retry-delay 5
        working-directory: ./python
      - name: Test Azure AI samples
        timeout-minutes: 10
@@ -460,7 +517,7 @@ jobs:
        python-tests-azure-openai,
        python-tests-misc-integration,
        python-tests-functions,
-        python-tests-azure-ai,
+        python-tests-foundry,
        python-tests-cosmos,
      ]
    steps:
@@ -23,10 +23,8 @@ jobs:
    environment: integration
    env:
      # Required configuration for get-started samples
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
+      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
+      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
    defaults:
      run:
        working-directory: python
@@ -41,6 +39,11 @@ jobs:
          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
          os: ${{ runner.os }}

+      - name: Create .env for samples
+        run: |
+          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
+          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
+
      - name: Run sample validation
        run: |
          cd scripts && uv run python -m sample_validation --subdir 01-get-started --save-report --report-name 01-get-started
@@ -50,24 +53,27 @@ jobs:
        if: always()
        with:
          name: validation-report-01-get-started
-          path: python/scripts/sample_validation/reports/
+          path: python/samples/sample_validation/reports/

  validate-02-agents:
    name: Validate 02-agents
    runs-on: ubuntu-latest
    environment: integration
    env:
-      # Azure AI configuration
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      # Foundry configuration
+      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
+      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # Azure OpenAI configuration
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME || vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
      # OpenAI configuration
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
+      # GitHub MCP
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
      # Observability
      ENABLE_INSTRUMENTATION: "true"
    defaults:
@@ -84,29 +90,150 @@ jobs:
          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
          os: ${{ runner.os }}

+      - name: Create .env for samples
+        run: |
+          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
+          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
+          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
+          echo "AZURE_OPENAI_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=$AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME" >> .env
+          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
+          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
+          echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
+          echo "GITHUB_PAT=$GITHUB_PAT" >> .env
+
      - name: Run sample validation
        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents --save-report --report-name 02-agents
+          cd scripts && uv run python -m sample_validation --subdir 02-agents --exclude providers --save-report --report-name 02-agents

      - name: Upload validation report
        uses: actions/upload-artifact@v7
        if: always()
        with:
          name: validation-report-02-agents
-          path: python/scripts/sample_validation/reports/
+          path: python/samples/sample_validation/reports/

-  validate-03-workflows:
-    name: Validate 03-workflows
+  validate-02-agents-openai:
+    name: Validate 02-agents/providers/openai
+    runs-on: ubuntu-latest
+    environment: integration
+    env:
+      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
+      OPENAI_MODEL: ${{ vars.OPENAI__CHATMODELID }}
+      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
+      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
+    defaults:
+      run:
+        working-directory: python
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Setup environment
+        uses: ./.github/actions/sample-validation-setup
+        with:
+          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
+          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+          os: ${{ runner.os }}
+
+      - name: Create .env for samples
+        run: |
+          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
+          echo "OPENAI_MODEL=$OPENAI_MODEL" >> .env
+          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
+          echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
+
+      - name: Run sample validation
+        run: |
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/openai --save-report --report-name 02-agents-openai
+
+      - name: Upload validation report
+        uses: actions/upload-artifact@v7
+        if: always()
+        with:
+          name: validation-report-02-agents-openai
+          path: python/samples/sample_validation/reports/
+
+  validate-02-agents-azure:
+    name: Validate 02-agents/providers/azure
    runs-on: ubuntu-latest
    environment: integration
    env:
-      # Azure AI configuration
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # Azure OpenAI configuration
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_API_VERSION: ${{ vars.AZURE_OPENAI_API_VERSION || '' }}
+    defaults:
+      run:
+        working-directory: python
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Setup environment
+        uses: ./.github/actions/sample-validation-setup
+        with:
+          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
+          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+          os: ${{ runner.os }}
+
+      - name: Create .env for samples
+        run: |
+          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
+          echo "AZURE_OPENAI_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_OPENAI_API_VERSION=$AZURE_OPENAI_API_VERSION" >> .env
+
+      - name: Run sample validation
+        run: |
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure --save-report --report-name 02-agents-azure
+
+      - name: Upload validation report
+        uses: actions/upload-artifact@v7
+        if: always()
+        with:
+          name: validation-report-02-agents-azure
+          path: python/samples/sample_validation/reports/
+
+  validate-02-agents-anthropic:
+    name: Validate 02-agents/providers/anthropic
+    runs-on: ubuntu-latest
+    environment: integration
+    env:
+      ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
+      ANTHROPIC_CHAT_MODEL_ID: ${{ vars.ANTHROPIC_CHAT_MODEL_ID }}
+    defaults:
+      run:
+        working-directory: python
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Setup environment
+        uses: ./.github/actions/sample-validation-setup
+        with:
+          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
+          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+          os: ${{ runner.os }}
+
+      - name: Create .env for samples
+        run: |
+          echo "ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY" >> .env
+          echo "ANTHROPIC_CHAT_MODEL_ID=$ANTHROPIC_CHAT_MODEL_ID" >> .env
+
+      - name: Run sample validation
+        run: |
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/anthropic --save-report --report-name 02-agents-anthropic
+
+      - name: Upload validation report
+        uses: actions/upload-artifact@v7
+        if: always()
+        with:
+          name: validation-report-02-agents-anthropic
+          path: python/samples/sample_validation/reports/
+
+  validate-02-agents-github-copilot:
+    name: Validate 02-agents/providers/github_copilot
+    runs-on: ubuntu-latest
+    environment: integration
    defaults:
      run:
        working-directory: python
@@ -121,6 +248,220 @@ jobs:
          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
          os: ${{ runner.os }}

+      - name: Run sample validation
+        run: |
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/github_copilot --save-report --report-name 02-agents-github-copilot
+
+      - name: Upload validation report
+        uses: actions/upload-artifact@v7
+        if: always()
+        with:
+          name: validation-report-02-agents-github-copilot
+          path: python/samples/sample_validation/reports/
+
+  validate-02-agents-amazon:
+    name: Validate 02-agents/providers/amazon
+    if: false # Temporarily disabled - requires AWS credentials
+    runs-on: ubuntu-latest
+    environment: integration
+    env:
+      BEDROCK_CHAT_MODEL_ID: ${{ vars.BEDROCK__CHATMODELID }}
+    defaults:
+      run:
+        working-directory: python
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Setup environment
+        uses: ./.github/actions/sample-validation-setup
+        with:
+          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
+          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+          os: ${{ runner.os }}
+
+      - name: Run sample validation
+        run: |
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/amazon --save-report --report-name 02-agents-amazon
+
+      - name: Upload validation report
+        uses: actions/upload-artifact@v7
+        if: always()
+        with:
+          name: validation-report-02-agents-amazon
+          path: python/samples/sample_validation/reports/
+
+  validate-02-agents-ollama:
+    name: Validate 02-agents/providers/ollama
+    if: false # Temporarily disabled - requires local Ollama server
+    runs-on: ubuntu-latest
+    environment: integration
+    env:
+      OLLAMA_MODEL: ${{ vars.OLLAMA__MODEL }}
+    defaults:
+      run:
+        working-directory: python
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Setup environment
+        uses: ./.github/actions/sample-validation-setup
+        with:
+          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
+          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+          os: ${{ runner.os }}
+
+      - name: Run sample validation
+        run: |
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/ollama --save-report --report-name 02-agents-ollama
+
+      - name: Upload validation report
+        uses: actions/upload-artifact@v7
+        if: always()
+        with:
+          name: validation-report-02-agents-ollama
+          path: python/samples/sample_validation/reports/
+
+  validate-02-agents-foundry:
+    name: Validate 02-agents/providers/foundry
+    if: false # Temporarily disabled - provider folder also contains the local Foundry sample
+    runs-on: ubuntu-latest
+    environment: integration
+    env:
+      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
+      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      FOUNDRY_AGENT_NAME: ${{ vars.FOUNDRY_AGENT_NAME || '' }}
+      FOUNDRY_AGENT_VERSION: ${{ vars.FOUNDRY_AGENT_VERSION || '' }}
+    defaults:
+      run:
+        working-directory: python
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Setup environment
+        uses: ./.github/actions/sample-validation-setup
+        with:
+          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
+          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+          os: ${{ runner.os }}
+
+      - name: Create .env for samples
+        run: |
+          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
+          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
+          echo "FOUNDRY_AGENT_NAME=$FOUNDRY_AGENT_NAME" >> .env
+          echo "FOUNDRY_AGENT_VERSION=$FOUNDRY_AGENT_VERSION" >> .env
+
+      - name: Run sample validation
+        run: |
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/foundry --save-report --report-name 02-agents-foundry
+
+      - name: Upload validation report
+        uses: actions/upload-artifact@v7
+        if: always()
+        with:
+          name: validation-report-02-agents-foundry
+          path: python/samples/sample_validation/reports/
+
+  validate-02-agents-copilotstudio:
+    name: Validate 02-agents/providers/copilotstudio
+    if: false # Temporarily disabled - requires Copilot Studio setup
+    runs-on: ubuntu-latest
+    environment: integration
+    env:
+      COPILOTSTUDIOAGENT__ENVIRONMENTID: ${{ secrets.COPILOTSTUDIOAGENT__ENVIRONMENTID }}
+      COPILOTSTUDIOAGENT__SCHEMANAME: ${{ secrets.COPILOTSTUDIOAGENT__SCHEMANAME }}
+      COPILOTSTUDIOAGENT__TENANTID: ${{ secrets.COPILOTSTUDIOAGENT__TENANTID }}
+      COPILOTSTUDIOAGENT__AGENTAPPID: ${{ secrets.COPILOTSTUDIOAGENT__AGENTAPPID }}
+    defaults:
+      run:
+        working-directory: python
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Setup environment
+        uses: ./.github/actions/sample-validation-setup
+        with:
+          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
+          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+          os: ${{ runner.os }}
+
+      - name: Create .env for samples
+        run: |
+          echo "COPILOTSTUDIOAGENT__ENVIRONMENTID=$COPILOTSTUDIOAGENT__ENVIRONMENTID" >> .env
+          echo "COPILOTSTUDIOAGENT__SCHEMANAME=$COPILOTSTUDIOAGENT__SCHEMANAME" >> .env
+          echo "COPILOTSTUDIOAGENT__TENANTID=$COPILOTSTUDIOAGENT__TENANTID" >> .env
+          echo "COPILOTSTUDIOAGENT__AGENTAPPID=$COPILOTSTUDIOAGENT__AGENTAPPID" >> .env
+
+      - name: Run sample validation
+        run: |
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/copilotstudio --save-report --report-name 02-agents-copilotstudio
+
+      - name: Upload validation report
+        uses: actions/upload-artifact@v7
+        if: always()
+        with:
+          name: validation-report-02-agents-copilotstudio
+          path: python/samples/sample_validation/reports/
+
+  validate-02-agents-custom:
+    name: Validate 02-agents/providers/custom
+    runs-on: ubuntu-latest
+    environment: integration
+    defaults:
+      run:
+        working-directory: python
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Setup environment
+        uses: ./.github/actions/sample-validation-setup
+        with:
+          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
+          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+          os: ${{ runner.os }}
+
+      - name: Run sample validation
+        run: |
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/custom --save-report --report-name 02-agents-custom
+
+      - name: Upload validation report
+        uses: actions/upload-artifact@v7
+        if: always()
+        with:
+          name: validation-report-02-agents-custom
+          path: python/samples/sample_validation/reports/
+
+  validate-03-workflows:
+    name: Validate 03-workflows
+    runs-on: ubuntu-latest
+    environment: integration
+    env:
+      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
+      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+    defaults:
+      run:
+        working-directory: python
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Setup environment
+        uses: ./.github/actions/sample-validation-setup
+        with:
+          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
+          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+          os: ${{ runner.os }}
+
+      - name: Create .env for samples
+        run: |
+          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
+          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
+
      - name: Run sample validation
        run: |
          cd scripts && uv run python -m sample_validation --subdir 03-workflows --save-report --report-name 03-workflows
@@ -130,20 +471,16 @@ jobs:
        if: always()
        with:
          name: validation-report-03-workflows
-          path: python/scripts/sample_validation/reports/
+          path: python/samples/sample_validation/reports/

  validate-04-hosting:
    name: Validate 04-hosting
-    if: false  # Temporarily disabled because of sample complexity
+    if: false # Temporarily disabled because of sample complexity
    runs-on: ubuntu-latest
    environment: integration
    env:
-      # Azure AI configuration
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      # Azure OpenAI configuration
-      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
+      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # A2A configuration
      A2A_AGENT_HOST: http://localhost:5001/
    defaults:
@@ -169,27 +506,26 @@ jobs:
        if: always()
        with:
          name: validation-report-04-hosting
-          path: python/scripts/sample_validation/reports/
+          path: python/samples/sample_validation/reports/

  validate-05-end-to-end:
    name: Validate 05-end-to-end
-    if: false  # Temporarily disabled because of sample complexity
+    if: false # Temporarily disabled because of sample complexity
    runs-on: ubuntu-latest
    environment: integration
    env:
-      # Azure AI configuration
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
+      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # Azure OpenAI configuration
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # Azure AI Search (for evaluation samples)
      AZURE_SEARCH_ENDPOINT: ${{ secrets.AZURE_SEARCH_ENDPOINT }}
      AZURE_SEARCH_API_KEY: ${{ secrets.AZURE_SEARCH_API_KEY }}
      AZURE_SEARCH_INDEX_NAME: ${{ secrets.AZURE_SEARCH_INDEX_NAME }}
      # Evaluation sample
-      AZURE_AI_MODEL_DEPLOYMENT_NAME_WORKFLOW: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      FOUNDRY_MODEL_WORKFLOW: ${{ vars.FOUNDRY_MODEL_WORKFLOW || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      FOUNDRY_MODEL_EVAL: ${{ vars.FOUNDRY_MODEL_EVAL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
    defaults:
      run:
        working-directory: python
@@ -213,23 +549,23 @@ jobs:
        if: always()
        with:
          name: validation-report-05-end-to-end
-          path: python/scripts/sample_validation/reports/
+          path: python/samples/sample_validation/reports/

  validate-autogen-migration:
    name: Validate autogen-migration
    runs-on: ubuntu-latest
    environment: integration
    env:
-      # Azure AI configuration
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
+      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # Azure OpenAI configuration
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
+      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # OpenAI configuration
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
+      OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
    defaults:
      run:
        working-directory: python
@@ -244,6 +580,16 @@ jobs:
          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
          os: ${{ runner.os }}

+      - name: Create .env for samples
+        run: |
+          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
+          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
+          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
+          echo "AZURE_OPENAI_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
+          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
+          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
+          echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
+
      - name: Run sample validation
        run: |
          cd scripts && uv run python -m sample_validation --subdir autogen-migration --save-report --report-name autogen-migration
@@ -253,24 +599,23 @@ jobs:
        if: always()
        with:
          name: validation-report-autogen-migration
-          path: python/scripts/sample_validation/reports/
+          path: python/samples/sample_validation/reports/

  validate-semantic-kernel-migration:
    name: Validate semantic-kernel-migration
    runs-on: ubuntu-latest
    environment: integration
    env:
-      # Azure AI configuration
-      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
-      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
+      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # Azure OpenAI configuration
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
-      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # OpenAI configuration
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
+      OPENAI_MODEL: ${{ vars.OPENAI__RESPONSESMODELID }}
      # Copilot Studio
      COPILOTSTUDIOAGENT__ENVIRONMENTID: ${{ secrets.COPILOTSTUDIOAGENT__ENVIRONMENTID }}
      COPILOTSTUDIOAGENT__SCHEMANAME: ${{ secrets.COPILOTSTUDIOAGENT__SCHEMANAME }}
@@ -290,6 +635,20 @@ jobs:
          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
          os: ${{ runner.os }}

+      - name: Create .env for samples
+        run: |
+          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
+          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
+          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
+          echo "AZURE_OPENAI_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
+          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
+          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
+          echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
+          echo "COPILOTSTUDIOAGENT__ENVIRONMENTID=$COPILOTSTUDIOAGENT__ENVIRONMENTID" >> .env
+          echo "COPILOTSTUDIOAGENT__SCHEMANAME=$COPILOTSTUDIOAGENT__SCHEMANAME" >> .env
+          echo "COPILOTSTUDIOAGENT__TENANTID=$COPILOTSTUDIOAGENT__TENANTID" >> .env
+          echo "COPILOTSTUDIOAGENT__AGENTAPPID=$COPILOTSTUDIOAGENT__AGENTAPPID" >> .env
+
      - name: Run sample validation
        run: |
          cd scripts && uv run python -m sample_validation --subdir semantic-kernel-migration --save-report --report-name semantic-kernel-migration
@@ -299,4 +658,67 @@ jobs:
        if: always()
        with:
          name: validation-report-semantic-kernel-migration
-          path: python/scripts/sample_validation/reports/
+          path: python/samples/sample_validation/reports/
+
+  aggregate-results:
+    name: Aggregate Results
+    runs-on: ubuntu-latest
+    if: always()
+    needs:
+      - validate-01-get-started
+      - validate-02-agents
+      - validate-02-agents-openai
+      - validate-02-agents-azure
+      - validate-02-agents-anthropic
+      - validate-02-agents-github-copilot
+      - validate-02-agents-amazon
+      - validate-02-agents-ollama
+      - validate-02-agents-foundry
+      - validate-02-agents-copilotstudio
+      - validate-02-agents-custom
+      - validate-03-workflows
+      - validate-04-hosting
+      - validate-05-end-to-end
+      - validate-autogen-migration
+      - validate-semantic-kernel-migration
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Download all validation reports
+        uses: actions/download-artifact@v7
+        with:
+          pattern: validation-report-*
+          path: reports/
+          merge-multiple: true
+
+      - name: Restore validation history
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: validation-history/
+          key: validation-history-${{ github.run_id }}
+          restore-keys: |
+            validation-history-
+
+      - name: Aggregate results and generate trend report
+        run: |
+          python3 python/scripts/sample_validation/aggregate.py \
+            reports/ \
+            validation-history/history.json \
+            trend-report.md
+
+      - name: Write trend report to job summary
+        run: cat trend-report.md >> "$GITHUB_STEP_SUMMARY"
+
+      - name: Save validation history
+        uses: actions/cache/save@v4
+        with:
+          path: validation-history/
+          key: validation-history-${{ github.run_id }}
+
+      - name: Upload trend report
+        uses: actions/upload-artifact@v7
+        if: always()
+        with:
+          name: validation-trend-report
+          path: trend-report.md
@@ -74,6 +74,37 @@ Contributions must maintain API signature and behavioral compatibility. Contribu
 that include breaking changes will be rejected. Please file an issue to discuss
 your idea or change if you believe that a breaking change is warranted.

+#### Automated API Compatibility Validation
+
+The .NET projects use [Package Validation](https://learn.microsoft.com/dotnet/fundamentals/package-validation/overview)
+to automatically detect API breaking changes. This validation runs during `dotnet build`
+(Release configuration) and `dotnet pack`, comparing the current API surface against the
+latest published NuGet baseline version.
+
+**What gets validated:** By default, packable RC packages (`IsReleaseCandidate=true`) and
+GA packages (`IsGenerallyAvailable=true`) that have a published NuGet baseline and do not
+override validation settings are automatically validated. The shared baseline version and
+default validation settings are defined in `dotnet/nuget/nuget-package.props`, but
+individual projects may opt out (for example by setting `EnablePackageValidation=false`).
+
+**If the build fails with CP errors (e.g., CP0001, CP0002):**
+
+1. **Unintentional breaking change** — Refactor your code to maintain backward compatibility.
+2. **Intentional breaking change** (approved by maintainers) — Generate a suppression file:
+   ```bash
+   dotnet build <project>.csproj -c Release /p:ApiCompatGenerateSuppressionFile=true
+   ```
+   This creates or updates a `CompatibilitySuppressions.xml` in the project directory.
+   Include this file in your PR with justification for the breaking change.
+
+**After each release:**
+
+1. Delete all `CompatibilitySuppressions.xml` files from validated projects.
+2. Update `PackageValidationBaselineVersion` in `dotnet/nuget/nuget-package.props` to the
+   newly published version.
+
+For more details, see the [Package Validation diagnostic IDs](https://learn.microsoft.com/dotnet/fundamentals/package-validation/diagnostic-ids).
+
 ### Suggested Workflow

 We use and recommend the following workflow:
@@ -92,22 +123,30 @@ We use and recommend the following workflow:
     "issue-123" or "githubhandle-issue".
 4. Make and commit your changes to your branch.
 5. Add new tests corresponding to your change, if applicable.
-6. Run the relevant scripts in [the section below](#development-scripts) to ensure that your build is clean and all tests are passing.
+6. Run the relevant scripts in [the section below](#development-setup) to ensure that your build is clean and all tests are passing.
 7. Create a PR against the repository's **main** branch.
   - State in the description what issue or improvement your change is addressing.
   - Verify that all the Continuous Integration checks are passing.
 8. Wait for feedback or approval of your changes from the code maintainers.
 9. When area owners have signed off, and all checks are green, your PR will be merged.

-### Development scripts
+### Development Setup

-The scripts below are used to build, test, and lint within the project.
+Each language has its own dev setup guide, coding standards, and build scripts:

- Python: see [python/DEV_SETUP.md](./python/DEV_SETUP.md).
- .NET:
-  - Build: `dotnet build`
-  - Test: `dotnet test`
-  - Linting (auto-fix): `dotnet format`
+- **Python**: [Dev Setup](./python/DEV_SETUP.md) · [Coding Standard](./python/CODING_STANDARD.md) · [README](./python/README.md)
+  - From the `./python` directory:
+    - Build: `uv run poe build`
+    - Unit tests: `uv run poe test -A -m "not integration"`
+    - Integration tests: `uv run poe test -A -m integration` (requires API keys/endpoints)
+    - Format + lint: `uv run poe syntax`
+    - All checks: `uv run poe check`
+- **.NET**: [README](./dotnet/README.md) · [Agent Instructions](./dotnet/AGENTS.md)
+  - From the `./dotnet` directory:
+    - Build: `dotnet build`
+    - Unit tests: `dotnet test --filter-query "/*UnitTests*/*/*/*"`
+    - Integration tests: `dotnet test --filter-query "/*IntegrationTests*/*/*/*"` (requires API keys/endpoints)
+    - Linting (auto-fix): `dotnet format`

 ### PR - CI Process

@@ -2,7 +2,7 @@

 # Welcome to Microsoft Agent Framework!

-[![Microsoft Azure AI Foundry Discord](https://dcbadge.limes.pink/api/server/b5zjErwbQM?style=flat)](https://discord.gg/b5zjErwbQM)
+[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/b5zjErwbQM?style=flat)](https://discord.gg/b5zjErwbQM)
 [![MS Learn Documentation](https://img.shields.io/badge/MS%20Learn-Documentation-blue)](https://learn.microsoft.com/en-us/agent-framework/)
 [![PyPI](https://img.shields.io/pypi/v/agent-framework)](https://pypi.org/project/agent-framework/)
 [![NuGet](https://img.shields.io/nuget/v/Microsoft.Agents.AI)](https://www.nuget.org/profiles/MicrosoftAgentFramework/)
@@ -137,24 +137,21 @@ var agent = new OpenAIClient("<apikey>")
 Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
 ```

-Create a simple Agent, using Azure OpenAI Responses with token based auth, that writes a haiku about the Microsoft Agent Framework
+Create a simple Agent, using Microsoft Foundry with token-based auth, that writes a haiku about the Microsoft Agent Framework

 ```c#
-// dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
+// dotnet add package Microsoft.Agents.AI.AzureAI --prerelease
 // dotnet add package Azure.Identity
 // Use `az login` to authenticate with Azure CLI
-using System.ClientModel.Primitives;
+using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using OpenAI;
-using OpenAI.Responses;

-// Replace <resource> and gpt-4o-mini with your Azure OpenAI resource name and deployment name.
-var agent = new OpenAIClient(
-    new BearerTokenPolicy(new AzureCliCredential(), "https://ai.azure.com/.default"),
-    new OpenAIClientOptions() { Endpoint = new Uri("https://<resource>.openai.azure.com/openai/v1") })
-    .GetResponsesClient("gpt-4o-mini")
-    .AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");
+var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+var agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
+    .AsAIAgent(model: deploymentName, name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");

 Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));
 ```
@@ -163,15 +160,43 @@ Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Fram

 ### Python

- [Getting Started with Agents](./python/samples/01-get-started): progressive tutorial from hello-world to hosting
+- [Getting Started](./python/samples/01-get-started): progressive tutorial from hello-world to hosting
 - [Agent Concepts](./python/samples/02-agents): deep-dive samples by topic (tools, middleware, providers, etc.)
- [Getting Started with Workflows](./python/samples/03-workflows): workflow creation and integration with agents
+- [Workflows](./python/samples/03-workflows): workflow creation and integration with agents
+- [Hosting](./python/samples/04-hosting): A2A, Azure Functions, Durable Task hosting
+- [End-to-End](./python/samples/05-end-to-end): full applications, evaluation, and demos

 ### .NET

- [Getting Started with Agents](./dotnet/samples/02-agents/Agents): basic agent creation and tool usage
- [Agent Provider Samples](./dotnet/samples/02-agents/AgentProviders): samples showing different agent providers
- [Workflow Samples](./dotnet/samples/03-workflows): advanced multi-agent patterns and workflow orchestration
+- [Getting Started](./dotnet/samples/01-get-started): progressive tutorial from hello agent to hosting
+- [Agent Concepts](./dotnet/samples/02-agents/Agents): basic agent creation and tool usage
+- [Agent Providers](./dotnet/samples/02-agents/AgentProviders): samples showing different agent providers
+- [Workflows](./dotnet/samples/03-workflows): advanced multi-agent patterns and workflow orchestration
+- [Hosting](./dotnet/samples/04-hosting): A2A, Durable Agents, Durable Workflows
+- [End-to-End](./dotnet/samples/05-end-to-end): full applications and demos
+
+## Troubleshooting
+
+### Authentication
+
+| Problem | Cause | Fix |
+|---------|-------|-----|
+| Authentication errors when using Azure credentials | Not signed in to Azure CLI | Run `az login` before starting your app |
+| API key errors | Wrong or missing API key | Verify the key and ensure it's for the correct resource/provider |
+
+> **Tip:** `DefaultAzureCredential` is convenient for development but in production, consider using a specific credential (e.g., `ManagedIdentityCredential`) to avoid latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+
+### Environment Variables
+
+The samples typically read configuration from environment variables. Common required variables:
+
+| Variable | Used by | Purpose |
+|----------|---------|---------|
+| `AZURE_OPENAI_ENDPOINT` | Azure OpenAI samples | Your Azure OpenAI resource URL |
+| `AZURE_OPENAI_DEPLOYMENT_NAME` | Azure OpenAI samples | Model deployment name (e.g. `gpt-4o-mini`) |
+| `AZURE_AI_PROJECT_ENDPOINT` | Microsoft Foundry samples | Your Microsoft Foundry project endpoint |
+| `AZURE_AI_MODEL_DEPLOYMENT_NAME` | Microsoft Foundry samples | Model deployment name |
+| `OPENAI_API_KEY` | OpenAI (non-Azure) samples | Your OpenAI platform API key |

 ## Contributor Resources

@@ -181,5 +206,9 @@ Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Fram
 - [Architectural Decision Records](./docs/decisions)

 ## Important Notes
-
-If you use the Microsoft Agent Framework to build applications that operate with third-party servers or agents, you do so at your own risk. We recommend reviewing all data being shared with third-party servers or agents and being cognizant of third-party practices for retention and location of data. It is your responsibility to manage whether your data will flow outside of your organization's Azure compliance and geographic boundaries and any related implications.
+> [!IMPORTANT]
+> If you use Microsoft Agent Framework to build applications that operate with any third-party servers, agents, code, or non-Azure Direct models (“Third-Party Systems”), you do so at your own risk. Third-Party Systems are Non-Microsoft Products under the Microsoft Product Terms and are governed by their own third-party license terms. You are responsible for any usage and associated costs.
+> 
+> We recommend reviewing all data being shared with and received from Third-Party Systems and being cognizant of third-party practices for handling, sharing, retention and location of data. It is your responsibility to manage whether your data will flow outside of your organization’s Azure compliance and geographic boundaries and any related implications, and that appropriate permissions, boundaries and approvals are provisioned.
+> 
+> You are responsible for carefully reviewing and testing applications you build using Microsoft Agent Framework in the context of your specific use cases, and making all appropriate decisions and customizations. This includes implementing your own responsible AI mitigations such as metaprompt, content filters, or other safety systems, and ensuring your applications meet appropriate quality, reliability, security, and trustworthiness standards. See also: [Transparency FAQ](./TRANSPARENCY_FAQ.md.md) 
@@ -0,0 +1,125 @@
+---
+status: accepted
+contact: rogerbarreto
+date: 2026-03-06
+deciders: rogerbarreto, alliscode
+consulted: ""
+informed: ""
+---
+
+# Foundry agent surface stays centered on `ChatClientAgent`
+
+## Context
+
+The Microsoft Foundry integration exposes two distinct usage patterns:
+
+1. Direct Responses usage, where callers provide model, instructions, and tools at runtime.
+2. Server-side versioned agents, where callers create and manage `AgentVersion` resources through `AIProjectClient.Agents`.
+
+We briefly explored adding public wrapper types such as `FoundryAgent`, `FoundryVersionedAgent`, and `FoundryResponsesChatClient` to make those paths feel more specialized. That direction created extra public types, duplicated existing `ChatClientAgent` behavior, and pushed samples toward compatibility helpers instead of the native Azure SDK flow.
+
+## Decision
+
+Keep the public surface centered on `ChatClientAgent`.
+
+- Direct Responses scenarios use `AIProjectClient.AsAIAgent(...)`.
+- Server-side versioned scenarios use native `AIProjectClient.Agents` APIs to create or retrieve agent resources, then wrap `AgentRecord` or `AgentVersion` with `AIProjectClient.AsAIAgent(...)`.
+- Compatibility helpers such as `AIProjectClient.CreateAIAgentAsync(...)` and `AIProjectClient.GetAIAgentAsync(...)` remain only as obsolete migration shims.
+- Public wrapper types `FoundryAgent`, `FoundryVersionedAgent`, `FoundryResponsesChatClient`, and `FoundryResponsesChatClientAgent` are not part of the chosen direction.
+
+## Why
+
+- `ChatClientAgent` is already the framework abstraction used everywhere else.
+- `AIProjectClient` is the native Azure SDK entry point for versioned agent lifecycle operations.
+- A single agent abstraction avoids parallel type hierarchies for the same backend.
+- Samples become clearer when they show either:
+  - direct Responses construction via `AIProjectClient.AsAIAgent(...)`, or
+  - native Foundry resource management via `AIProjectClient.Agents`.
+
+## Consequences
+
+### Direct Responses path
+
+Use the convenience overloads on `AIProjectClient`:
+
+```csharp
+AIProjectClient aiProjectClient = new(new Uri(endpoint), credential);
+
+ChatClientAgent agent = aiProjectClient.AsAIAgent(
+    model: deploymentName,
+    instructions: "You are good at telling jokes.",
+    name: "JokerAgent");
+```
+
+Or use composed `ChatClientAgent`
+
+```csharp
+ProjectResponsesClient projectResponsesClient = new(new Uri(endpoint), new DefaultAzureCredential(), new AgentReference($"model:{deploymentName}"));
+
+ChatClientAgent agent = new(
+    chatClient: projectResponsesClient.AsIChatClient(),
+    instructions: "You are good at telling jokes.",
+    name: "JokerAgent");
+```
+
+This path is code-first and does not create a persistent server-side agent.
+
+### Versioned agent path
+
+Use the convenience overloads on `AIProjectClient`:
+
+```csharp
+AIProjectClient aiProjectClient = new(new Uri(endpoint), credential);
+
+AgentVersion version = await aiProjectClient.Agents.CreateAgentVersionAsync(
+    "JokerAgent",
+    new AgentVersionCreationOptions(
+        new PromptAgentDefinition(deploymentName)
+        {
+            Instructions = "You are good at telling jokes."
+        }));
+
+ChatClientAgent agent = aiProjectClient.AsAIAgent(version);
+```
+
+Or use composed `ChatClientAgent`
+
+```csharp
+AIProjectClient aiProjectClient = new(new Uri(endpoint), credential);
+
+AgentVersion version = await aiProjectClient.Agents.CreateAgentVersionAsync(
+    "JokerAgent",
+    new AgentVersionCreationOptions(
+        new PromptAgentDefinition(deploymentName)
+        {
+            Instructions = "You are good at telling jokes."
+        }));
+
+ProjectResponsesClient projectResponsesClient = aiProjectClient
+    .GetProjectOpenAIClient()
+    .GetProjectResponsesClientForAgent(new AgentReference(version.Name, version.Version));
+
+ChatClientAgent agent = new(
+    chatClient: projectResponsesClient.AsIChatClient(),
+    name: "JokerAgent");
+```
+
+### Samples
+
+- `FoundryAgents/` samples show the direct Responses path with `AIProjectClient.AsAIAgent(...)`.
+- `FoundryVersionedAgents/` samples should show native `AIProjectClient.Agents` create/get/delete flows plus `AsAIAgent(...)`.
+
+### Compatibility APIs
+
+Obsolete helper extensions remain only to ease migration of existing code. New samples and new guidance should not be written against them.
+
+## Rejected direction
+
+Do not introduce or preserve separate public wrapper types whose main purpose is to forward to `ChatClientAgent` while carrying Foundry-specific naming.
+
+That approach:
+
+- duplicates lifecycle concepts already present on `AIProjectClient`,
+- fragments the public API,
+- complicates samples and docs,
+- and makes migration harder by encouraging wrapper-specific affordances.
@@ -0,0 +1,960 @@
+status: proposed
+date: 2026-03-23
+contact: sergeymenshykh
+deciders: rbarreto, westey-m, eavanvalkenburg
+---
+
+# Agent Skills: Multi-Source Architecture
+
+## Context and Problem Statement
+
+The Agent Framework needs a skills system that lets agents discover and use domain-specific knowledge, reference documents, and executable scripts. Skills can originate from different sources — filesystem directories (SKILL.md files), inline C# code, or reusable class libraries — and the framework must support all three uniformly while allowing extensibility, composition, and filtering.
+
+## Decision Drivers
+
+- Skills must be definable from multiple sources: filesystem, inline code, reusable classes, etc
+- Common abstractions are needed so the provider and builder work uniformly regardless of skill origin
+- File-based scripts must support user-defined executors, enabling custom runtimes and languages; code/class-based scripts execute in-process as C# delegates
+- Skills must be filterable so consumers can include or exclude specific skills based on defined criteria
+- Multiple skill sources must be composable into a single provider
+- It must be possible to add custom skill sources (e.g., databases, REST APIs, package registries) by implementing a common abstraction
+
+## Architecture
+
+### Model-Facing Tools
+
+Skills are presented to the model as up to three tools that progressively disclose skill content. The system prompt lists available skill names and descriptions; the model then calls these tools on demand:
+
+- **`load_skill(skillName)`** — returns the full skill body (instructions, listed resources, listed scripts)
+- **`read_skill_resource(skillName, resourceName)`** — reads a supplementary resource (file-based or code-defined) associated with a skill
+- **`run_skill_script(skillName, scriptName, arguments?)`** — executes a script associated with a skill; only registered when at least one skill contains scripts
+
+Each tool delegates to the corresponding method on the resolved `AgentSkill` — calling `Resource.ReadAsync()` or `Script.RunAsync()` respectively.
+
+If skills have no scripts defined, the `run_skill_script` tool is **not advertised** to the model and instructions related to script execution are **not included** in the default skills instructions.
+
+### Abstract Base Types
+
+The architecture defines four abstract base types that all skill variants implement:
+
+```csharp
+public abstract class AgentSkill
+{
+    public abstract AgentSkillFrontmatter Frontmatter { get; }
+    public abstract string Content { get; }
+    public abstract IReadOnlyList<AgentSkillResource>? Resources { get; }
+    public abstract IReadOnlyList<AgentSkillScript>? Scripts { get; }
+}
+
+public abstract class AgentSkillResource
+{
+    public string Name { get; }
+    public string? Description { get; }
+    public abstract Task<object?> ReadAsync(IServiceProvider? serviceProvider = null, CancellationToken cancellationToken = default);
+}
+
+public abstract class AgentSkillScript
+{
+    public string Name { get; }
+    public string? Description { get; }
+    public abstract Task<object?> RunAsync(AgentSkill skill, AIFunctionArguments arguments, CancellationToken cancellationToken = default);
+}
+
+public abstract class AgentSkillsSource
+{
+    public abstract Task<IList<AgentSkill>> GetSkillsAsync(CancellationToken cancellationToken = default);
+}
+```
+
+Skill metadata is captured via `AgentSkillFrontmatter`:
+
+```csharp
+public sealed class AgentSkillFrontmatter
+{
+    public AgentSkillFrontmatter(string name, string description) { ... }
+
+    public string Name { get; }
+    public string Description { get; }
+    public string? License { get; set; }
+    public string? Compatibility { get; set; }
+    public string? AllowedTools { get; set; }
+    public AdditionalPropertiesDictionary? Metadata { get; set; }
+}
+```
+
+The type hierarchy at a glance:
+
+```
+AgentSkill (abstract)                   AgentSkillsSource (abstract)
+├── AgentFileSkill                      ├── AgentFileSkillsSource          (public)
+└── [Programmatic]                      ├── AgentInMemorySkillsSource      (public)
+    ├── AgentInlineSkill                ├── AggregatingAgentSkillsSource     (public)
+    └── AgentClassSkill (abstract)      └── DelegatingAgentSkillsSource    (abstract, public)
+                                            ├── FilteringAgentSkillsSource  (public)
+AgentSkillResource (abstract)               ├── CachingAgentSkillsSource    (public)
+├── AgentFileSkillResource                  └── DeduplicatingAgentSkillsSource (public)
+└── AgentInlineSkillResource
+                                        AgentSkillScript (abstract)
+                                        ├── AgentFileSkillScript
+                                        └── AgentInlineSkillScript
+```
+
+There are two top-level categories of skills:
+
+1. **File-Based Skills** — discovered from `SKILL.md` files on the filesystem. Resources and scripts are files in subdirectories.
+2. **Programmatic Skills** — defined in C# code. These are further divided into:
+   - **Inline Skills** — built at runtime via the `AgentInlineSkill` class and its fluent API. Ideal for quick, agent-specific skill definitions.
+   - **Class-Based Skills** — defined as reusable C# classes that subclass `AgentClassSkill`. Ideal for packaging skills as shared libraries or NuGet packages.
+
+Both programmatic skill types use `AgentInlineSkillResource` and `AgentInlineSkillScript` for their resources and scripts. They are typically served by `AgentInMemorySkillsSource`, which accepts any `AgentSkill` and is not limited to programmatic skills.
+
+### File-Based Skills
+
+File-based skills are authored as `SKILL.md` files on disk. Resources and scripts are discovered from corresponding subfolders within the skill directory.
+
+**`AgentFileSkill`** — A filesystem-based skill discovered from a directory containing a `SKILL.md` file. Parsed from YAML frontmatter; content is the raw markdown body. Resources and scripts are discovered from files in corresponding subfolders:
+
+```csharp
+public sealed class AgentFileSkill : AgentSkill
+{
+    internal AgentFileSkill(
+        AgentSkillFrontmatter frontmatter, string content, string path,
+        IReadOnlyList<AgentSkillResource>? resources = null,
+        IReadOnlyList<AgentSkillScript>? scripts = null) { ... }
+}
+```
+
+**`AgentFileSkillResource`** — A file-based skill resource. Reads content from a file on disk relative to the skill directory:
+
+```csharp
+internal sealed class AgentFileSkillResource : AgentSkillResource
+{
+    public AgentFileSkillResource(string name, string fullPath) { ... }
+
+    public string FullPath { get; }
+
+    public override Task<object?> ReadAsync(IServiceProvider? serviceProvider = null, CancellationToken cancellationToken = default)
+    {
+        return File.ReadAllTextAsync(FullPath, Encoding.UTF8, cancellationToken);
+    }
+}
+```
+
+**`AgentFileSkillScript`** — A file-based skill script that represents a script file on disk. Delegates execution to an external `AgentFileSkillScriptRunner` callback (e.g., runs Python/shell via `Process.Start`). Throws `NotSupportedException` if no executor is configured:
+
+```csharp
+public delegate Task<object?> AgentFileSkillScriptRunner(
+    AgentFileSkill skill, AgentFileSkillScript script,
+    AIFunctionArguments arguments, CancellationToken cancellationToken);
+
+public sealed class AgentFileSkillScript : AgentSkillScript
+{
+    private readonly AgentFileSkillScriptRunner _executor;
+
+    internal AgentFileSkillScript(string name, string fullPath, AgentFileSkillScriptRunner executor)
+        : base(name) { ... }
+
+    public override async Task<object?> RunAsync(AgentSkill skill, AIFunctionArguments arguments, ...)
+    {
+
+        return await _executor(fileSkill, this, arguments, cancellationToken);
+    }
+}
+```
+
+The executor can be provided at the **provider level** via `AgentSkillsProviderBuilder.UseFileScriptRunner(executor)` and optionally overridden for a **particular file skill** or for a **set of skills** at the file skill source level, giving fine-grained control over how different scripts are executed.
+
+**`AgentFileSkillsSource`** — A skill source that discovers skills from filesystem directories containing `SKILL.md` files. Recursively scans directories (max 2 levels), validates frontmatter, and enforces path traversal and symlink security checks:
+
+```csharp
+public sealed partial class AgentFileSkillsSource : AgentSkillsSource
+{
+    public AgentFileSkillsSource(
+        IEnumerable<string> skillPaths,
+        AgentFileSkillScriptRunner scriptRunner,
+        AgentFileSkillsSourceOptions? options = null,
+        ILoggerFactory? loggerFactory = null) { ... }
+}
+```
+
+**`AgentFileSkillsSourceOptions`** — Configuration options for `AgentFileSkillsSource`. Allows customizing the allowed file extensions for resources and scripts without adding constructor parameters:
+
+```csharp
+public sealed class AgentFileSkillsSourceOptions
+{
+    public IEnumerable<string>? AllowedResourceExtensions { get; set; }
+    public IEnumerable<string>? AllowedScriptExtensions { get; set; }
+}
+```
+
+**Example** — A file-based skill on disk and how it is added to a source:
+
+```
+skills/
+└── unit-converter/
+    ├── SKILL.md               # frontmatter + instructions
+    ├── resources/
+    │   └── conversion-table.csv   # discovered as a resource
+    └── scripts/
+        └── convert.py             # discovered as a script
+```
+
+```csharp
+var source = new AgentFileSkillsSource(skillPaths: ["./skills"], scriptRunner: SubprocessScriptRunner.RunAsync);
+
+var provider = new AgentSkillsProvider(source);
+
+AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions
+{
+    AIContextProviders = [provider],
+});
+```
+
+### Programmatic Skills
+
+Programmatic skills are defined in C# code rather than discovered from the filesystem. There are two kinds: **inline** and **class-based**. Both use `AgentInlineSkillResource` and `AgentInlineSkillScript` for resources and scripts, and are held by a single `AgentInMemorySkillsSource`.
+
+**`AgentInMemorySkillsSource`** — A general-purpose skill source that holds any `AgentSkill` instances in memory. Although commonly used for programmatic skills (`AgentInlineSkill` and `AgentClassSkill`), it accepts any `AgentSkill` subclass and is not restricted to code-defined skills:
+
+```csharp
+public sealed class AgentInMemorySkillsSource : AgentSkillsSource
+{
+    public AgentInMemorySkillsSource(
+        IEnumerable<AgentSkill> skills,
+        ILoggerFactory? loggerFactory = null) { ... }
+}
+```
+
+#### Inline Skills
+
+Inline skills are built at runtime via the `AgentInlineSkill` class and its fluent API. They are ideal for quick, agent-specific skill definitions where a full class hierarchy would be overkill.
+
+**`AgentInlineSkill`** — A skill defined entirely in code. Resources can be static values or functions; scripts are always functions. Constructed with name, description, and instructions, then extended with resources and scripts:
+
+```csharp
+public sealed class AgentInlineSkill : AgentSkill
+{
+    public AgentInlineSkill(string name, string description, string instructions, string? license = null, string? compatibility = null, ...) { ... }
+    public AgentInlineSkill(AgentSkillFrontmatter frontmatter, string instructions) { ... }
+
+    public AgentInlineSkill AddResource(object value, string name, string? description = null);
+    public AgentInlineSkill AddResource(Delegate handler, string name, string? description = null);
+    public AgentInlineSkill AddScript(Delegate handler, string name, string? description = null);
+}
+```
+
+**`AgentInlineSkillResource`** — A skill resource that wraps a static value:
+
+```csharp
+public sealed class AgentInlineSkillResource : AgentSkillResource
+{
+    public AgentInlineSkillResource(object value, string name, string? description = null)
+        : base(name, description)
+    {
+        _value = value;
+    }
+
+    public override Task<object?> ReadAsync(IServiceProvider? serviceProvider = null, CancellationToken cancellationToken = default)
+    {
+        return Task.FromResult<object?>(_value);
+    }
+}
+```
+
+**`AgentInlineSkillResource`** — A skill resource backed by a delegate. The delegate is invoked via an `AIFunction` each time `ReadAsync` is called, producing a dynamic (computed) value:
+
+```csharp
+public sealed class AgentInlineSkillResource : AgentSkillResource
+{
+    public AgentInlineSkillResource(Delegate handler, string name, string? description = null)
+        : base(name, description)
+    {
+        _function = AIFunctionFactory.Create(handler, name: name);
+    }
+
+    public override async Task<object?> ReadAsync(IServiceProvider? serviceProvider = null, CancellationToken cancellationToken = default)
+    {
+        return await _function.InvokeAsync(new AIFunctionArguments() { Services = serviceProvider }, cancellationToken);
+    }
+}
+```
+
+**`AgentInlineSkillScript`** — A skill script backed by a delegate via an `AIFunction`:
+
+```csharp
+public sealed class AgentInlineSkillScript : AgentSkillScript
+{
+    private readonly AIFunction _function;
+
+    public AgentInlineSkillScript(Delegate handler, string name, string? description = null)
+        : base(name, description)
+    {
+        _function = AIFunctionFactory.Create(handler, name: name);
+    }
+
+    public JsonElement? ParametersSchema => _function.JsonSchema;
+
+    public override async Task<object?> RunAsync(AgentSkill skill, AIFunctionArguments arguments, ...)
+    {
+        return await _function.InvokeAsync(arguments, cancellationToken);
+    }
+}
+```
+
+**Example** — Creating an inline skill with a resource and script, then adding it to a source:
+
+```csharp
+var skill = new AgentInlineSkill(
+        name: "unit-converter",
+        description: "Converts between measurement units.",
+        instructions: """
+            Use this skill to convert values between metric and imperial units.
+            Refer to the conversion-table resource for supported unit pairs.
+            Run the convert script to perform conversions.
+            """
+    )
+    .AddResource("kg=2.205lb, m=3.281ft, L=0.264gal", "conversion-table", "Supported unit pairs")
+    .AddScript(Convert, "convert", "Converts a value between units");
+
+var source = new AgentInMemorySkillsSource([skill]);
+
+var provider = new AgentSkillsProvider(source);
+
+AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions
+{
+    AIContextProviders = [provider],
+});
+
+static string Convert(double value, double factor)
+    => JsonSerializer.Serialize(new { result = Math.Round(value * factor, 4) });
+```
+
+#### Class-Based Skills
+
+Class-based skills are designed for packaging skills as reusable libraries. Users subclass `AgentClassSkill` and override properties. Unlike inline skills, class-based skills are self-contained, can live in shared libraries or NuGet packages, and are well-suited for dependency injection.
+
+**`AgentClassSkill`** — An abstract base class for defining skills as reusable C# classes that bundle all skill components (frontmatter, instructions, resources, scripts) together. Designed for packaging skills as distributable libraries:
+
+```csharp
+public abstract class AgentClassSkill : AgentSkill
+{
+    public abstract string Instructions { get; }
+
+    // Content is auto-synthesized from Frontmatter + Instructions + Resources + Scripts
+    public override string Content =>
+        SkillContentBuilder.BuildContent(Frontmatter.Name, Frontmatter.Description,
+            SkillContentBuilder.BuildBody(Instructions, Resources, Scripts));
+}
+```
+
+**Example** — Defining a class-based skill and adding it to a source:
+
+```csharp
+public class UnitConverterSkill : AgentClassSkill
+{
+    public override AgentSkillFrontmatter Frontmatter { get; } =
+        new("unit-converter", "Converts between measurement units.");
+
+    public override string Instructions => """
+        Use this skill to convert values between metric and imperial units.
+        Refer to the conversion-table resource for supported unit pairs.
+        Run the convert script to perform conversions.
+        """;
+
+    public override IReadOnlyList<AgentSkillResource>? Resources { get; } =
+    [
+        new AgentInlineSkillResource("kg=2.205lb, m=3.281ft", "conversion-table"),
+    ];
+
+    public override IReadOnlyList<AgentSkillScript>? Scripts { get; } =
+    [
+        new AgentInlineSkillScript(Convert, "convert"),
+    ];
+
+    private static string Convert(double value, double factor)
+        => JsonSerializer.Serialize(new { result = Math.Round(value * factor, 4) });
+}
+
+var source = new AgentInMemorySkillsSource([new UnitConverterSkill()]);
+
+var provider = new AgentSkillsProvider(source);
+
+AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions
+{
+    AIContextProviders = [provider],
+});
+```
+
+## Filtering, Caching, and Deduplication
+
+The following subsections present alternative approaches for handling filtering, caching, and deduplication of skills across multiple sources.
+
+### Via Composition
+
+In this approach, the `AgentSkillsProvider` accepts a **single** `AgentSkillsSource`. Multiple sources are composed externally via an aggregate source, and cross-cutting concerns like filtering, caching, and deduplication are implemented as **source decorators** — subclasses of `DelegatingAgentSkillsSource` that intercept `GetSkillsAsync()`.
+
+**`FilteringAgentSkillsSource`** — A decorator that applies filter logic before returning results. The decorator pattern keeps filtering orthogonal to source implementations and allows composing multiple filters:
+
+```csharp
+public sealed class FilteringAgentSkillsSource : DelegatingAgentSkillsSource
+{
+    private readonly Func<AgentSkill, bool> _predicate;
+
+    public FilteringAgentSkillsSource(AgentSkillsSource innerSource, Func<AgentSkill, bool> predicate)
+        : base(innerSource)
+    {
+        _predicate = predicate;
+    }
+
+    public override async Task<IList<AgentSkill>> GetSkillsAsync(CancellationToken cancellationToken = default)
+    {
+        var skills = await this.InnerSource.GetSkillsAsync(cancellationToken);
+        return skills.Where(_predicate).ToList();
+    }
+}
+```
+
+**`CachingAgentSkillsSource`** — A decorator that caches skills after the first load, keeping the provider stateless and giving consumers control over caching granularity per source. For example, file-based skills (expensive to discover) can be cached while code-defined skills remain uncached:
+
+```csharp
+public sealed class CachingAgentSkillsSource : DelegatingAgentSkillsSource
+{
+    private IList<AgentSkill>? _cached;
+
+    public CachingAgentSkillsSource(AgentSkillsSource innerSource)
+        : base(innerSource)
+    {
+    }
+
+    public override async Task<IList<AgentSkill>> GetSkillsAsync(CancellationToken cancellationToken = default)
+    {
+        return _cached ??= await this.InnerSource.GetSkillsAsync(cancellationToken);
+    }
+}
+```
+
+**Deduplication** is similarly implemented as a decorator (`DeduplicatingAgentSkillsSource`) that deduplicates by name (case-insensitive, first-one-wins) and logs a warning for skipped duplicates.
+
+**Example** — Combining file-based and code-defined sources with filtering and caching:
+
+```csharp
+var fileSource = new CachingAgentSkillsSource(new AgentFileSkillsSource(["./skills"]));
+var codeSource = new AgentInMemorySkillsSource([myCodeSkill]);
+
+var compositeSource = new FilteringAgentSkillsSource(
+    new AggregatingAgentSkillsSource([fileSource, codeSource]),
+    filter: s => s.Frontmatter.Name != "internal");
+
+var provider = new AgentSkillsProvider(compositeSource);
+
+AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions
+{
+    AIContextProviders = [provider],
+});
+```
+
+**Pros:**
+- Clean single-responsibility: the provider serves skills, sources provide them.
+- Caching, filtering, and deduplication are composable as source decorators — each concern is a separate, testable wrapper.
+
+**Cons:**
+- DI is less flexible: multiple `AgentSkillsSource` implementations registered in the container cannot be auto-injected into the provider. The consumer must manually compose them via an aggregate source.
+- Increased public API surface: requires additional public classes (aggregate source, caching decorators, filtering decorators) that consumers need to learn and use.
+
+### Via AgentSkillsProvider
+
+In this approach, the `AgentSkillsProvider` accepts **`IEnumerable<AgentSkillsSource>`** and handles aggregation, filtering, caching, and deduplication internally.
+
+The provider aggregates skills from all registered sources, deduplicates by name (case-insensitive, first-one-wins), caches the result after the first load, and optionally applies filtering via a predicate on `AgentSkillsProviderOptions`. Duplicate skill names are logged as warnings.
+
+**Example** — Registering multiple sources directly with the provider:
+
+```csharp
+// Conceptual example — in practice, use AgentSkillsProviderBuilder
+var fileSource = new AgentFileSkillsSource(["./skills"]);
+var codeSource = new AgentInMemorySkillsSource([myCodeSkill]);
+
+var provider = new AgentSkillsProvider(
+    sources: [fileSource, codeSource],
+    options: new AgentSkillsProviderOptions
+    {
+        Filter = s => s.Frontmatter.Name != "internal",
+    });
+
+AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions
+{
+    AIContextProviders = [provider],
+});
+```
+
+**Pros:**
+- DI-friendly: register multiple `AgentSkillsSource` implementations in the container, and they are all auto-injected into `AgentSkillsProvider` via `IEnumerable<AgentSkillsSource>`.
+- Smaller public API surface: no need for aggregate source, caching decorators, or filtering decorator classes — these concerns are handled internally by the provider.
+
+**Cons:**
+- The provider takes on multiple responsibilities — aggregation, caching, deduplication, and filtering.
+- Less granular caching control: caching is all-or-nothing across sources rather than per-source as with decorators.
+- Less extensible: new behaviors (e.g., ordering, TTL expiration) require modifying the provider rather than adding a decorator.
+
+### Builder Pattern
+
+**`AgentSkillsProviderBuilder`** provides a fluent API for composing skills from multiple sources. The builder centralizes configuration — script executors, approval callbacks, prompt templates, and filtering — so consumers don't need to know the underlying source types.
+
+The builder internally decides how to wire up the object graph: it creates the appropriate source instances, applies caching and filtering, and returns a fully configured `AgentSkillsProvider`. This keeps the setup code concise while still allowing fine-grained control when needed.
+
+**Example** — Using the builder to combine multiple source types with configuration:
+
+```csharp
+var provider = new AgentSkillsProviderBuilder()
+    .UseFileSkill("./skills")                           // file-based source
+    .UseInlineSkills(codeSkill)                            // code-defined source
+    .UseClassSkills(new ClassSkill())                    // class-based source
+    .UseFileScriptRunner(SubprocessScriptRunner.RunAsync) // script runner
+    .UseScriptApproval()                                // optional human-in-the-loop
+    .UsePromptTemplate(customTemplate)                  // optional prompt customization
+    .UseFilter(s => s.Frontmatter.Name != "internal")   // optional skill filtering
+    .Build();
+
+AIAgent agent = chatClient.AsAIAgent(new ChatClientAgentOptions
+{
+    AIContextProviders = [provider],
+});
+```
+
+## Adding a Custom Skill Type
+
+The skills framework is designed for extensibility. While file-based and inline skills cover common
+scenarios, you can introduce entirely new skill types by subclassing the four base classes:
+
+| Base class            | Purpose                                             |
+|-----------------------|-----------------------------------------------------|
+| `AgentSkillsSource`   | Discovers and loads skills from a particular origin  |
+| `AgentSkill`          | Holds metadata, content, resources, and scripts      |
+| `AgentSkillResource`  | Provides supplementary content to a skill            |
+| `AgentSkillScript`    | Represents an executable action within a skill       |
+
+The example below implements a **cloud-based skill type** where skills, resources, and scripts are
+all stored in and executed through a remote cloud service (e.g., Azure Blob Storage + Azure Functions).
+
+### Step 1 — Define a custom resource
+
+A `CloudSkillResource` reads resource content from a cloud storage endpoint instead of the local
+filesystem:
+
+```csharp
+/// <summary>
+/// A skill resource backed by a cloud storage endpoint.
+/// </summary>
+public sealed class CloudSkillResource : AgentSkillResource
+{
+    private readonly HttpClient _httpClient;
+
+    public CloudSkillResource(string name, Uri blobUri, HttpClient httpClient, string? description = null)
+        : base(name, description)
+    {
+        BlobUri = blobUri ?? throw new ArgumentNullException(nameof(blobUri));
+        _httpClient = httpClient ?? throw new ArgumentNullException(nameof(httpClient));
+    }
+
+    /// <summary>
+    /// Gets the URI of the cloud blob that holds this resource's content.
+    /// </summary>
+    public Uri BlobUri { get; }
+
+    /// <inheritdoc/>
+    public override async Task<object?> ReadAsync(
+        IServiceProvider? serviceProvider = null,
+        CancellationToken cancellationToken = default)
+    {
+        return await _httpClient.GetStringAsync(BlobUri, cancellationToken).ConfigureAwait(false);
+    }
+}
+```
+
+### Step 2 — Define a custom script
+
+A `CloudSkillScript` executes a script by calling a cloud function endpoint, passing arguments as
+the request body:
+
+```csharp
+/// <summary>
+/// A skill script executed via a cloud function endpoint.
+/// </summary>
+public sealed class CloudSkillScript : AgentSkillScript
+{
+    private readonly HttpClient _httpClient;
+
+    public CloudSkillScript(string name, Uri functionUri, HttpClient httpClient, string? description = null)
+        : base(name, description)
+    {
+        FunctionUri = functionUri ?? throw new ArgumentNullException(nameof(functionUri));
+        _httpClient = httpClient ?? throw new ArgumentNullException(nameof(httpClient));
+    }
+
+    /// <summary>
+    /// Gets the URI of the cloud function that runs this script.
+    /// </summary>
+    public Uri FunctionUri { get; }
+
+    /// <inheritdoc/>
+    public override async Task<object?> RunAsync(
+        AgentSkill skill,
+        AIFunctionArguments arguments,
+        CancellationToken cancellationToken = default)
+    {
+        var json = JsonSerializer.Serialize(arguments);
+        using var content = new StringContent(json, Encoding.UTF8, "application/json");
+        var response = await _httpClient.PostAsync(FunctionUri, content, cancellationToken)
+            .ConfigureAwait(false);
+        response.EnsureSuccessStatusCode();
+        return await response.Content.ReadAsStringAsync(cancellationToken).ConfigureAwait(false);
+    }
+}
+```
+
+### Step 3 — Define a custom skill
+
+A `CloudSkill` bundles cloud-specific metadata (e.g., the base endpoint) with the standard skill
+shape:
+
+```csharp
+/// <summary>
+/// An <see cref="AgentSkill"/> whose content, resources, and scripts are stored in a cloud service.
+/// </summary>
+public sealed class CloudSkill : AgentSkill
+{
+    public CloudSkill(
+        AgentSkillFrontmatter frontmatter,
+        string content,
+        Uri endpoint,
+        IReadOnlyList<AgentSkillResource>? resources = null,
+        IReadOnlyList<AgentSkillScript>? scripts = null)
+    {
+        Frontmatter = frontmatter ?? throw new ArgumentNullException(nameof(frontmatter));
+        Content = content ?? throw new ArgumentNullException(nameof(content));
+        Endpoint = endpoint ?? throw new ArgumentNullException(nameof(endpoint));
+        Resources = resources;
+        Scripts = scripts;
+    }
+
+    /// <inheritdoc/>
+    public override AgentSkillFrontmatter Frontmatter { get; }
+
+    /// <inheritdoc/>
+    public override string Content { get; }
+
+    /// <summary>
+    /// Gets the base cloud endpoint for this skill.
+    /// </summary>
+    public Uri Endpoint { get; }
+
+    /// <inheritdoc/>
+    public override IReadOnlyList<AgentSkillResource>? Resources { get; }
+
+    /// <inheritdoc/>
+    public override IReadOnlyList<AgentSkillScript>? Scripts { get; }
+}
+```
+
+### Step 4 — Define a custom source
+
+A `CloudSkillsSource` discovers skills from a cloud catalog API and constructs `CloudSkill`
+instances with their associated resources and scripts:
+
+```csharp
+/// <summary>
+/// A skill source that discovers and loads skills from a cloud catalog API.
+/// </summary>
+public sealed class CloudSkillsSource : AgentSkillsSource
+{
+    private readonly Uri _catalogUri;
+    private readonly HttpClient _httpClient;
+
+    public CloudSkillsSource(Uri catalogUri, HttpClient httpClient)
+    {
+        _catalogUri = catalogUri ?? throw new ArgumentNullException(nameof(catalogUri));
+        _httpClient = httpClient ?? throw new ArgumentNullException(nameof(httpClient));
+    }
+
+    /// <inheritdoc/>
+    public override async Task<IList<AgentSkill>> GetSkillsAsync(
+        CancellationToken cancellationToken = default)
+    {
+        // Fetch the skill catalog from the cloud service.
+        var json = await _httpClient.GetStringAsync(_catalogUri, cancellationToken)
+            .ConfigureAwait(false);
+        var catalog = JsonSerializer.Deserialize<CloudSkillCatalog>(json)!;
+
+        var skills = new List<AgentSkill>();
+
+        foreach (var entry in catalog.Skills)
+        {
+            var frontmatter = new AgentSkillFrontmatter(entry.Name, entry.Description);
+
+            // Build cloud-backed resources.
+            var resources = entry.Resources
+                .Select(r => new CloudSkillResource(r.Name, r.BlobUri, _httpClient, r.Description))
+                .ToList<AgentSkillResource>();
+
+            // Build cloud-backed scripts.
+            var scripts = entry.Scripts
+                .Select(s => new CloudSkillScript(s.Name, s.FunctionUri, _httpClient, s.Description))
+                .ToList<AgentSkillScript>();
+
+            skills.Add(new CloudSkill(frontmatter, entry.Content, entry.Endpoint, resources, scripts));
+        }
+
+        return skills;
+    }
+}
+```
+
+### Step 5 — Register with the builder
+
+Use `UseSource` to wire the custom source into the provider:
+
+```csharp
+var httpClient = new HttpClient();
+
+var provider = new AgentSkillsProviderBuilder()
+    .UseSource(new CloudSkillsSource(
+        new Uri("https://my-service.example.com/skills/catalog"),
+        httpClient))
+    // Mix with other source types if needed:
+    .UseFileSkill("/local/skills", scriptRunner)
+    .UseInlineSkills(someInlineSkill)
+    .Build();
+```
+
+The `AgentSkillsProvider` handles all skill types uniformly — any combination of file-based, inline,
+class-based, and custom skills can coexist in the same provider. Custom skills automatically
+participate in the model-facing tools (`load_skill`, `read_skill_resource`, `run_skill_script`),
+filtering, deduplication, and caching — no additional integration work is required.
+
+## Script Representation: `AgentSkillScript` vs `AIFunction`
+
+Two approaches were considered for representing executable scripts within skills:
+
+### Option A — Custom `AgentSkillScript` abstract base class (original design)
+
+Scripts are modeled as a custom `AgentSkillScript` abstract class with `Name`, `Description`, and
+`RunAsync(AgentSkill, AIFunctionArguments, CancellationToken)`. Concrete implementations:
+`AgentInlineSkillScript` (wraps a delegate/`AIFunction`) and `AgentFileSkillScript` (wraps a file path + executor delegate).
+
+```csharp
+// Base type
+public abstract class AgentSkillScript
+{
+    public string Name { get; }
+    public string? Description { get; }
+    public abstract Task<object?> RunAsync(AgentSkill skill, AIFunctionArguments arguments, CancellationToken cancellationToken = default);
+}
+
+// AgentSkill exposes scripts as:
+public abstract IReadOnlyList<AgentSkillScript>? Scripts { get; }
+
+// Inline script wraps an AIFunction internally
+var script = new AgentInlineSkillScript(ConvertUnits, "convert");
+
+// Pre-built AIFunction must be wrapped
+var script = new AgentInlineSkillScript(myAIFunction);
+
+// Class-based skill declares scripts as:
+public override IReadOnlyList<AgentSkillScript>? Scripts { get; } =
+[
+    new AgentInlineSkillScript(ConvertUnits, "convert"),
+];
+
+// Provider executes scripts by passing the owning skill:
+await script.RunAsync(skill, arguments, cancellationToken);
+```
+
+**Pros:**
+
+- **Explicit skill context at execution time.** `RunAsync` receives the owning `AgentSkill`, so any script can access skill metadata or resources during execution without requiring construction-time wiring.
+- **Self-contained abstraction.** A dedicated type communicates clearly that scripts are a skills-framework concept, separate from general-purpose AI functions.
+- **Easier extensibility for custom script types.** Third-party implementations can subclass `AgentSkillScript` and access the owning skill in `RunAsync` without special setup.
+
+**Cons:**
+
+- **Wrapper overhead.** `AgentInlineSkillScript` is a thin pass-through around `AIFunction` — it adds a class, a constructor, and an indirection layer for no behavioral difference.
+- **Parallel abstraction.** `AgentSkillScript` and `AIFunction` serve overlapping purposes (named callable with arguments), creating two parallel hierarchies for the same concept.
+- **Friction for consumers.** Users who already have `AIFunction` instances must wrap them in `AgentInlineSkillScript` to use them as scripts, adding ceremony.
+
+### Option B — Reuse `AIFunction` directly
+
+Scripts are represented as `AIFunction` (from `Microsoft.Extensions.AI`). `AgentSkill.Scripts` returns
+`IReadOnlyList<AIFunction>?`. `AgentInlineSkillScript` is eliminated entirely — callers use
+`AIFunctionFactory.Create(delegate, name: ...)` or pass `AIFunction` instances directly.
+`AgentFileSkillScript` becomes an `AIFunction` subclass that captures its owning `AgentFileSkill` via
+an internal back-reference set during construction.
+
+```csharp
+// AgentSkill exposes scripts as AIFunction directly:
+public abstract IReadOnlyList<AIFunction>? Scripts { get; }
+
+// Inline scripts use AIFunctionFactory — no wrapper class needed
+var skill = new AgentInlineSkill("my-skill", "desc", "instructions");
+skill.AddScript(ConvertUnits, "convert");           // delegate
+skill.AddScript(myAIFunction);                       // pre-built AIFunction — no wrapping
+
+// Class-based skill declares scripts as:
+public override IReadOnlyList<AIFunction>? Scripts { get; } =
+[
+    AIFunctionFactory.Create(ConvertUnits, name: "convert"),
+];
+
+// Provider executes scripts via standard AIFunction invocation:
+await script.InvokeAsync(arguments, cancellationToken);
+
+// File-based scripts extend AIFunction and capture the owning skill internally:
+public sealed class AgentFileSkillScript : AIFunction
+{
+    internal AgentFileSkill? Skill { get; set; }   // set by AgentFileSkill constructor
+
+    protected override async ValueTask<object?> InvokeCoreAsync(
+        AIFunctionArguments arguments, CancellationToken cancellationToken)
+    {
+        return await _executor(Skill!, this, arguments, cancellationToken);
+    }
+}
+```
+
+**Pros:**
+
+- **Fewer types.** Eliminates `AgentSkillScript` and `AgentInlineSkillScript`, reducing the public API surface by two classes.
+- **Seamless interop.** Any `AIFunction` — whether from `AIFunctionFactory`, a custom subclass, or an external library — can be used as a skill script with zero wrapping.
+- **Consistent with `Microsoft.Extensions.AI` ecosystem.** Scripts share the same type as tool functions used by `IChatClient` and `FunctionInvokingChatClient`, reducing conceptual overhead for developers already familiar with the ecosystem.
+
+**Cons:**
+
+- **No owning-skill context in invocation signature.** `AIFunction.InvokeAsync` does not accept an `AgentSkill` parameter, so `AgentFileSkillScript` must capture its owning skill via an internal setter during construction. This adds a construction-order dependency: the skill must set the back-reference on its scripts.
+- **Custom script types lose automatic skill access.** Third-party `AIFunction` subclasses that need the owning skill must implement their own mechanism (e.g., constructor injection, closure capture) instead of receiving it as a method parameter.
+- **Semantic overloading.** `AIFunction` now means both "a tool the model can call" and "a script within a skill", which could blur the distinction for framework users.
+
+## Resource Representation: `AgentSkillResource` vs `AIFunction`
+
+Two approaches were considered for representing skill resources (supplementary content such as references, assets, or dynamic data):
+
+### Option A — Custom `AgentSkillResource` abstract base class (original design)
+
+Resources are modeled as a custom `AgentSkillResource` abstract class with `Name`, `Description`, and
+`ReadAsync(IServiceProvider?, CancellationToken)`. Concrete implementations:
+`AgentInlineSkillResource` (static value, delegate, or `AIFunction` wrapper) and `AgentFileSkillResource` (reads file content from disk).
+
+```csharp
+// Base type
+public abstract class AgentSkillResource
+{
+    public string Name { get; }
+    public string? Description { get; }
+    public abstract Task<object?> ReadAsync(IServiceProvider? serviceProvider = null, CancellationToken cancellationToken = default);
+}
+
+// AgentSkill exposes resources as:
+public abstract IReadOnlyList<AgentSkillResource>? Resources { get; }
+
+// Static resource
+var resource = new AgentInlineSkillResource("static content", "my-resource");
+
+// Dynamic resource (delegate)
+var resource = new AgentInlineSkillResource((IServiceProvider sp) => GetData(sp), "my-resource");
+
+// Pre-built AIFunction must be wrapped
+var resource = new AgentInlineSkillResource(myAIFunction);
+
+// Class-based skill declares resources as:
+public override IReadOnlyList<AgentSkillResource>? Resources { get; } =
+[
+    new AgentInlineSkillResource("# Conversion Tables\n...", "conversion-table"),
+];
+
+// Provider reads resources via:
+await resource.ReadAsync(serviceProvider, cancellationToken);
+```
+
+**Pros:**
+
+- **Clear semantic distinction.** A dedicated `AgentSkillResource` type distinguishes resources (data providers) from scripts (executable actions), making the API self-documenting.
+- **Purpose-built API.** `ReadAsync` communicates intent better than `InvokeAsync` for a data-access operation.
+
+**Cons:**
+
+- **Wrapper overhead.** `AgentInlineSkillResource` wraps `AIFunction` internally for delegate/function cases — adding a class and indirection for no behavioral difference.
+- **Parallel abstraction.** `AgentSkillResource` and `AIFunction` serve overlapping purposes (named callable that returns data), creating two parallel hierarchies.
+- **Friction for consumers.** Users who already have `AIFunction` instances must wrap them in `AgentInlineSkillResource`, adding ceremony.
+
+### Option B — Reuse `AIFunction` directly
+
+Resources are represented as `AIFunction`. `AgentSkill.Resources` returns `IReadOnlyList<AIFunction>?`.
+`AgentInlineSkillResource` becomes an `AIFunction` subclass (retained as a convenience for the static-value
+pattern: `new AgentInlineSkillResource("data", "name")`). `AgentFileSkillResource` becomes an `AIFunction`
+subclass that reads file content.
+
+```csharp
+// AgentSkill exposes resources as AIFunction directly:
+public abstract IReadOnlyList<AIFunction>? Resources { get; }
+
+// Static resource — AgentInlineSkillResource is retained as a convenience AIFunction subclass
+var resource = new AgentInlineSkillResource("static content", "my-resource");
+
+// Dynamic resource — AgentInlineSkillResource wraps delegate as AIFunction
+var resource = new AgentInlineSkillResource((IServiceProvider sp) => GetData(sp), "my-resource");
+
+// Pre-built AIFunction can be used directly — no wrapping needed
+skill.AddResource(myAIFunction);
+
+// Class-based skill declares resources as:
+public override IReadOnlyList<AIFunction>? Resources { get; } =
+[
+    new AgentInlineSkillResource("# Conversion Tables\n...", "conversion-table"),
+];
+
+// Provider reads resources via standard AIFunction invocation:
+await resource.InvokeAsync(arguments, cancellationToken);
+
+// File-based resources extend AIFunction directly:
+internal sealed class AgentFileSkillResource : AIFunction
+{
+    public string FullPath { get; }
+
+    protected override async ValueTask<object?> InvokeCoreAsync(
+        AIFunctionArguments arguments, CancellationToken cancellationToken)
+    {
+        return await File.ReadAllTextAsync(FullPath, Encoding.UTF8, cancellationToken);
+    }
+}
+```
+
+**Pros:**
+
+- **Fewer base types.** Eliminates the `AgentSkillResource` abstract class, reducing the public API surface.
+- **Seamless interop.** Any `AIFunction` can be used as a skill resource with zero wrapping.
+
+**Cons:**
+
+- **Loss of semantic distinction.** Resources and scripts are now both `AIFunction`, which could make it less obvious which list a function belongs to when reading code.
+- **Static values require a wrapper.** Unlike the original `ReadAsync` which could return a stored value directly, `AIFunction.InvokeAsync` implies invocation. `AgentInlineSkillResource` is retained as a convenience subclass to handle the static-value case, so this is not eliminated — just moved to a different class.
+
+## Decision Outcome
+
+### 1. Keep `AgentSkillResource` and `AgentSkillScript` (Option A for both sections)
+
+We are staying with the custom `AgentSkillResource` and `AgentSkillScript` model classes instead of reusing `AIFunction`:
+
+- **Resources have no parameters.** If a consumer provides an `AIFunction` with parameters, those parameters will never be advertised to the LLM, and the resulting call will fail.
+- **Approval breaks for `AIFunction`-based representations.** When a resource or script represented by an `AIFunction` is configured with approval, the second approval invocation will not work correctly.
+- **Injecting the owning skill into an `AIFunction`-based script is problematic.** Constructor injection would introduce a circular reference between the skill and the script. An internal property setter is possible but adds coupling.
+
+### 2. Make all agent skill classes internal
+
+All agent-skill-related classes are made `internal` to minimize the public API surface while the feature matures. We can reconsider and promote types to `public` later based on community signal.
+
+This leaves two public entry points:
+
+- **`AgentSkillsProvider`** — use directly when all skills come from a single source and filtering is not needed.
+- **`AgentSkillsProviderBuilder`** — use when mixing skill types or when filtering support is required.
+
+### 3. Caching at provider level
+
+Caching of tools and instructions is implemented inside `AgentSkillsProvider` rather than as an external decorator. Recreating tools and instructions on every provider call is wasteful, and a caching decorator sitting outside the provider would not have the information needed to cache them effectively.
@@ -0,0 +1,72 @@
+---
+status: accepted
+contact: eavanvalkenburg
+date: 2026-03-20
+deciders: eavanvalkenburg, sphenry, chetantoshnival
+consulted: taochenosu, moonbox3, dmytrostruk, giles17, alliscode
+---
+
+# Provider-Leading Client Design & OpenAI Package Extraction
+
+## Context and Problem Statement
+
+The `agent-framework-core` package currently bundles OpenAI and Azure OpenAI client implementations along with their dependencies (`openai`, `azure-identity`, `azure-ai-projects`, `packaging`). This makes core heavier than necessary for users who don't use OpenAI, and it conflates the core abstractions with a specific provider implementation. Additionally, the current class naming (`OpenAIResponsesClient`, `OpenAIChatClient`) is based on the underlying OpenAI API names rather than what users actually want to do, making discoverability harder for newcomers.
+
+## Decision Drivers
+
+- **Lightweight core**: Core should only contain abstractions, middleware infrastructure, and telemetry — no provider-specific code or dependencies.
+- **Discoverability-first**: Import namespaces should guide users to the right client. `from agent_framework.openai import ...` should surface all OpenAI-related clients; `from agent_framework.azure import ...` should surface Foundry, Azure AI, and other Azure-specific classes.
+- **Provider-leading naming**: The primary client name should reflect the provider, not the underlying API. The Responses API is now the recommended default for OpenAI, so its client should be called `OpenAIChatClient` (not `OpenAIResponsesClient`).
+- **Clean separation of concerns**: Azure-specific deprecated wrappers belong in the azure-ai package, not in the OpenAI package.
+
+## Considered Options
+
+- **Keep OpenAI in core**: Simpler but keeps core heavy; doesn't help discoverability.
+- **Extract OpenAI with Azure wrappers in the OpenAI package**: Keeps Azure OpenAI wrappers alongside OpenAI code, but pollutes the OpenAI package with Azure concerns.
+- **Extract OpenAI, place Azure wrappers in azure-ai**: Clean separation; the OpenAI package has zero Azure dependencies; deprecated Azure wrappers live in a single file in azure-ai for easy future deletion.
+
+## Decision Outcome
+
+Chosen option: "Extract OpenAI, place Azure wrappers in azure-ai", because it achieves the lightest core, cleanest OpenAI package, and the most maintainable deprecation path.
+
+Key changes:
+
+1. **New `agent-framework-openai` package** with dependencies on `agent-framework-core`, `openai`, and `packaging` only.
+2. **Class renames**: `OpenAIResponsesClient` → `OpenAIChatClient` (Responses API), `OpenAIChatClient` → `OpenAIChatCompletionClient` (Chat Completions API). Old names remain as deprecated aliases.
+3. **Deprecated classes**: `OpenAIAssistantsClient`, all `AzureOpenAI*Client` classes, `AzureAIClient`, `AzureAIAgentClient`, and `AzureAIProjectAgentProvider` are marked deprecated.
+4. **New `FoundryChatClient`** in azure-ai for Azure AI Foundry Responses API access, built on `RawFoundryChatClient(RawOpenAIChatClient)`.
+5. **All deprecated `AzureOpenAI*` classes** consolidated into a single file (`_deprecated_azure_openai.py`) in the azure-ai package for clean future deletion.
+6. **Core's `agent_framework.openai` and `agent_framework.azure` namespaces** become lazy-loading gateways, preserving backward-compatible import paths while removing hard dependencies.
+7. **Unified `model` parameter** replaces `model_id` (OpenAI), `deployment_name` (Azure OpenAI), and `model_deployment_name` (Azure AI) across all client constructors. The term `model` is intentionally generic: it naturally maps to an OpenAI model name *and* to an Azure OpenAI deployment name, making it straightforward to use `OpenAIChatClient` with either OpenAI or Azure OpenAI backends (via `AsyncAzureOpenAI`). Environment variables are similarly unified (e.g., `OPENAI_MODEL` instead of separate `OPENAI_RESPONSES_MODEL_ID` / `OPENAI_CHAT_MODEL_ID`).
+8. **`FoundryAgent`** replaces the pattern of `Agent(client=AzureAIClient(...))` for connecting to pre-configured agents in Azure AI Foundry (PromptAgents and HostedAgents). The underlying `RawFoundryAgentChatClient` is an implementation detail — most users interact only with `FoundryAgent`. `AzureAIAgentClient` is separately deprecated as it refers to the V1 Agents Service API. See below for design rationale.
+
+### Foundry Agent Design: `FoundryAgentClient` vs `FoundryAgent`
+
+The existing `AzureAIClient` combines two concerns: CRUD lifecycle management (creating/deleting agents on the service) and runtime communication (sending messages via the Responses API). The new design removes CRUD entirely — users connect to agents that already exist in Foundry.
+
+**Two approaches were considered:**
+
+**Option A — `FoundryAgentClient` only (public ChatClient):**
+Users compose `Agent(client=FoundryAgentClient(...), tools=[...])`. This follows the universal `Agent(client=X)` pattern used by every other provider. However, a "client" that wraps a named remote agent (with `agent_name` as a constructor param) is semantically odd — clients typically wrap a model endpoint, not a specific agent.
+
+**Option B — `FoundryAgent` (Agent subclass) + private `_FoundryAgentChatClient` and public `RawFoundryAgentChatClient`:**
+Users write `FoundryAgent(agent_name="my-agent", ...)` for the common case. Internally, `FoundryAgent` creates a `_FoundryAgentChatClient` and passes it to the standard `Agent` base class. For advanced customization, users pass `client_type=RawFoundryAgentChatClient` (or a custom subclass) to control the client middleware layers. The `Agent(client=RawFoundryAgentChatClient(...))` composition pattern still works for users who prefer it.
+
+**Chosen option: Option B**, because:
+- The common case (`FoundryAgent(...)`) is a single object with no boilerplate.
+- `client_type=` gives full control over client middleware without parameter duplication — the agent forwards connection params to the client internally.
+- `RawFoundryAgent(RawAgent)` and `FoundryAgent(Agent)` mirror the established `RawAgent`/`Agent` pattern.
+- Runtime validation (only `FunctionTool` allowed) lives in `RawFoundryAgentChatClient._prepare_options`, ensuring it applies regardless of how the client is used — through `FoundryAgent`, `Agent(client=...)`, or any custom composition.
+
+**Public classes:**
+- `RawFoundryAgentChatClient(RawOpenAIChatClient)` — Responses API client that injects agent reference and validates tools. Extension point for custom client middleware.
+- `RawFoundryAgent(RawAgent)` — Agent without agent-level middleware/telemetry.
+- `FoundryAgent(AgentTelemetryLayer, AgentMiddlewareLayer, RawFoundryAgent)` — Recommended production agent.
+
+**Internal (private):**
+- `_FoundryAgentChatClient` — Full client with function invocation, chat middleware, and telemetry layers. Created automatically by `FoundryAgent`; users customize via `client_type=RawFoundryAgentChatClient` or a custom subclass.
+
+**Deprecated:**
+- `AzureAIClient` — replaced by `FoundryAgent` (which uses `FoundryAgentClient` internally).
+- `AzureAIAgentClient` — refers to V1 Agents Service API, no direct replacement.
+- `AzureAIProjectAgentProvider` — replaced by `FoundryAgent`.
@@ -0,0 +1,121 @@
+---
+status: accepted
+contact: westey-m
+date: 2026-03-23
+deciders: sergeymenshykh, markwallace, rbarreto, dmytrostruk, westey-m, eavanvalkenburg, stephentoub
+consulted:
+informed:
+---
+
+# Chat History Persistence Consistency
+
+## Context and Problem Statement
+
+When using `ChatClientAgent` with tools, the `FunctionInvokingChatClient` (FIC) loops multiple times — service call → tool execution → service call → … — before producing a final response. There are two points of discrepancy between how chat history is stored by the framework's `ChatHistoryProvider` and how the underlying AI service stores chat history (e.g., OpenAI Responses with `store=true`):
+
+1. **Persistence timing**: The AI service persists messages after *each* service call within the FIC loop. The `ChatHistoryProvider` currently persists messages only once, at the *end* of the full agent run (after all FIC loop iterations complete).
+
+2. **Trailing `FunctionResultContent` storage**: When tool calling is terminated mid-loop (e.g., via `FunctionInvokingChatClient` termination filters), the final response from the agent may contain `FunctionResultContent` that was never sent to a subsequent service call. The AI service never stores this trailing `FunctionResultContent`, but the `ChatHistoryProvider` currently stores all response content, including the trailing `FunctionResultContent`.
+
+These discrepancies mean that a `ChatHistoryProvider`-managed conversation and a service-managed conversation can diverge in content and structure, even when processing the same interactions.
+
+### Practical Impact: Resuming After Tool-Call Termination
+
+Today, users of `AIAgent` get different behaviors depending on whether chat history is stored service-side or in a `ChatHistoryProvider`. This creates concrete challenges — for example, when the function call loop is terminated and the user wants to resume the conversation in a subsequent run. With service-stored history, the trailing `FunctionResultContent` is never persisted, so the last stored message is the `FunctionCallContent` from the service. With `ChatHistoryProvider`-stored history, the trailing `FunctionResultContent` *is* persisted. The user cannot know whether the last `FunctionResultContent` is in the chat history or not without inspecting the storage mechanism, making it difficult to write resumption logic that works correctly regardless of the storage backend.
+
+### Relationship Between the Two Discrepancies
+
+The persistence timing and `FunctionResultContent` trimming behaviors are interrelated:
+
+- **Per-service-call persistence**: When messages are persisted after each individual service call, trailing `FunctionResultContent` trimming is unnecessary. If tool calling is terminated, the `FunctionResultContent` from the terminated call was never sent to a subsequent service call, so it is never persisted. The per-service-call approach naturally matches the service's behavior.
+
+- **Per-run persistence**: When messages are batched and persisted at the end of the full run, trailing `FunctionResultContent` trimming becomes necessary to match the service's behavior. Without trimming, the stored history contains `FunctionResultContent` that the service would never have stored.
+
+## Decision Drivers
+
+- **A. Consistency**: The default behavior of `ChatHistoryProvider` should produce stored history that closely matches what the underlying AI service would store, minimizing surprise when switching between framework-managed and service-managed chat history.
+- **B. Atomicity**: A run that fails mid-way through a multi-step tool-calling loop should not leave chat history in a partially-updated state, unless the user explicitly opts into that behavior.
+- **C. Recoverability**: For long-running tool-calling loops, it should be possible to recover intermediate progress if the process is interrupted, rather than losing all work from the current run.
+- **D. Simplicity**: The default behavior should be easy to understand and predict for most users, without requiring knowledge of the FIC loop internals.
+- **E. Flexibility**: Regardless of the chosen default, users should be able to opt into the alternative behavior.
+
+## Considered Options
+
+- Option 1: Per-run persistence with opt-in FRC (FunctionResultContent) trimming
+- Option 2: Opt-in per-service-call persistence (via `RequirePerServiceCallChatHistoryPersistence`)
+
+## Pros and Cons of the Options
+
+### Option 1: Per-run persistence with opt-in FRC trimming
+
+Keep the current default behavior of persisting chat history only at the end of the full agent run. Add `FunctionResultContent` trimming as an opt-in behavior to improve consistency with service storage.
+
+- Good, because runs are atomic — chat history is only updated when the full run succeeds, satisfying driver B.
+- Good, because the mental model is simple: one run = one history update, satisfying driver D.
+- Good, because trimming trailing `FunctionResultContent` improves consistency with service storage, partially satisfying driver A.
+- Bad, because the default persistence timing still differs from the service's behavior (per-run vs. per-service-call), only partially satisfying driver A.
+- Bad, because if the process crashes mid-loop, all intermediate progress from the current run is lost, not satisfying driver C.
+- Bad, because this option alone does not provide a way for users to opt into per-service-call persistence, not satisfying driver E.
+
+### Option 2: Opt-in per-service-call persistence (via `RequirePerServiceCallChatHistoryPersistence`)
+
+Introduce an optional RequirePerServiceCallChatHistoryPersistence setting to persist chat history after each individual service call within the FIC loop, matching the AI service's behavior. Trailing `FunctionResultContent` trimming is unnecessary with this approach (it is naturally handled).
+
+Settings:
+- `RequirePerServiceCallChatHistoryPersistence` = `true`
+
+- Good, because the stored history matches the service's behavior when opting in for both timing and content, fully satisfying driver A.
+- Good, because intermediate progress is preserved if the process is interrupted, satisfying driver C.
+- Good, because no separate `FunctionResultContent` trimming logic is needed, reducing complexity.
+- Bad, because chat history may be left in an incomplete state if the run fails mid-loop (e.g., `FunctionCallContent` stored without corresponding `FunctionResultContent`), not satisfying driver B. A subsequent run cannot proceed without manually providing the missing `FunctionResultContent`.
+- Bad, because the mental model is more complex: a single run may produce multiple history updates, partially failing driver D.
+- Neutral, because users can opt out to per-run persistence if they prefer atomicity, satisfying driver E.
+
+## Decision Outcome
+
+Chosen option: **Option 2: Opt-in per-service-call persistence (via `RequirePerServiceCallChatHistoryPersistence`)**. The existing per-run persistence behavior is retained as-is, requiring no changes from users. Per-service-call persistence is available as an opt-in feature via the `RequirePerServiceCallChatHistoryPersistence` setting. This satisfies drivers B (atomicity) and D (simplicity) for the common case, while fully satisfying driver A (consistency) for users who opt into simulated service-stored behavior. Users who need per-service-call persistence for recoverability (driver C) can enable it explicitly.
+
+### Configuration Matrix
+
+The behavior depends on the combination of `UseProvidedChatClientAsIs` and `RequirePerServiceCallChatHistoryPersistence`:
+
+| `UseProvidedChatClientAsIs` | `RequirePerServiceCallChatHistoryPersistence` | Behavior |
+|---|---|---|
+| `false` (default) | `false` (default) | **Per-run persistence.** Messages are persisted at the end of the full agent run via the `ChatHistoryProvider`. |
+| `false` | `true` | **Per-service-call persistence (simulated).** A `PerServiceCallChatHistoryPersistingChatClient` middleware is automatically injected into the chat client pipeline between `FunctionInvokingChatClient` and the leaf `IChatClient`. Messages are persisted after each service call. A sentinel `ConversationId` causes FIC to treat the conversation as service-managed. |
+| `true` | `false` | **Per-run persistence.** No middleware is injected because the user has provided a custom chat client stack. Messages are persisted at the end of the run. |
+| `true` | `true` | **User responsibility.** The system checks whether the custom chat client stack includes a `PerServiceCallChatHistoryPersistingChatClient`. If not, a warning is emitted — the user is expected to have added their own per-service-call persistence mechanism. End-of-run persistence is skipped. |
+
+### Consequences
+
+- Good, because per-run persistence is atomic by default — chat history is only updated when the full run succeeds, satisfying driver B.
+- Good, because the default mental model is simple: one run = one history update, satisfying driver D.
+- Good, because users who opt into `RequirePerServiceCallChatHistoryPersistence` get stored history that matches the service's behavior for both timing and content, fully satisfying driver A.
+- Good, because per-service-call persistence preserves intermediate progress if the process is interrupted, satisfying driver C when opted in.
+- Good, because no separate `FunctionResultContent` trimming logic is needed when per-service-call persistence is active — it is naturally handled.
+- Good, because conflict detection (configurable via `ThrowOnChatHistoryProviderConflict`, `WarnOnChatHistoryProviderConflict`, `ClearOnChatHistoryProviderConflict`) prevents misconfiguration when a service returns a `ConversationId` alongside a configured `ChatHistoryProvider`.
+- Bad, because per-service-call persistence (when opted in) may leave chat history in an incomplete state if the run fails mid-loop (e.g., `FunctionCallContent` stored without corresponding `FunctionResultContent`), requiring manual recovery in rare cases.
+- Neutral, because users who want per-service-call consistency can opt in via `RequirePerServiceCallChatHistoryPersistence = true`, satisfying driver E.
+- Neutral, because increased write frequency from per-service-call persistence may impact performance for some storage backends; this can be mitigated with a caching decorator.
+
+### Implementation Notes
+
+#### Conversation ID Consistency
+
+When `RequirePerServiceCallChatHistoryPersistence` is enabled, the `PerServiceCallChatHistoryPersistingChatClient`
+decorator also updates `session.ConversationId` after each service call. This handles two scenarios:
+
+1. **Framework-managed chat history** — the decorator sets a sentinel `ConversationId` on the response
+   so that `FunctionInvokingChatClient` treats the conversation as service-managed (clearing accumulated
+   history between iterations and not injecting duplicate `FunctionCallContent` during approval processing).
+
+2. **Service-stored chat history** — when the service returns a real `ConversationId`, the decorator
+   updates `session.ConversationId` immediately after each service call, rather than deferring the update
+   to the end of the run. This ensures intermediate ConversationId changes are captured even if the
+   process is interrupted mid-loop.
+
+For some service-stored scenarios (e.g., the Conversations API with the Responses API), there is only
+one thread with one ID, so every service call returns the same ConversationId and this per-call update
+makes no practical difference. Enabling `RequirePerServiceCallChatHistoryPersistence` ensures consistent
+per-service-call behavior across all service types regardless of how they manage ConversationIds.
+
@@ -462,7 +462,7 @@ class FoundryEvals:
 ### Azure AI: FoundryEvals Constants

 ```python
-from agent_framework_azure_ai import FoundryEvals
+from agent_framework.foundry import FoundryEvals

 evaluators = [FoundryEvals.RELEVANCE, FoundryEvals.TOOL_CALL_ACCURACY]
 ```
@@ -17,6 +17,7 @@

  <PropertyGroup>
    <IsReleaseCandidate>false</IsReleaseCandidate>
+    <IsGenerallyAvailable>false</IsGenerallyAvailable>
  </PropertyGroup>
  
  <PropertyGroup>
@@ -1,4 +1,4 @@
-<Solution>
+<Solution>
  <Configurations>
    <BuildType Name="Debug" />
    <BuildType Name="Publish" />
@@ -57,6 +57,7 @@
    <Project Path="samples/02-agents/Agents/Agent_Step16_Declarative/Agent_Step16_Declarative.csproj" />
    <Project Path="samples/02-agents/Agents/Agent_Step17_AdditionalAIContext/Agent_Step17_AdditionalAIContext.csproj" />
    <Project Path="samples/02-agents/Agents/Agent_Step18_CompactionPipeline/Agent_Step18_CompactionPipeline.csproj" />
+    <Project Path="samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing/Agent_Step19_InFunctionLoopCheckpointing.csproj" />
  </Folder>
  <Folder Name="/Samples/02-agents/DeclarativeAgents/">
    <Project Path="samples/02-agents/DeclarativeAgents/ChatClient/DeclarativeChatClientAgents.csproj" />
@@ -76,6 +77,8 @@
    <Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/01_SequentialWorkflow/01_SequentialWorkflow.csproj" />
    <Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/02_ConcurrentWorkflow/02_ConcurrentWorkflow.csproj" />
    <Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/03_WorkflowHITL/03_WorkflowHITL.csproj" />
+    <Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/04_WorkflowMcpTool/04_WorkflowMcpTool.csproj" />
+    <Project Path="samples/04-hosting/DurableWorkflows/AzureFunctions/05_WorkflowAndAgents/05_WorkflowAndAgents.csproj" />
  </Folder>
  <Folder Name="/Samples/GettingStarted/">
    <File Path="samples/GettingStarted/README.md" />
@@ -101,7 +104,8 @@
  </Folder>
  <Folder Name="/Samples/02-agents/AgentSkills/">
    <File Path="samples/02-agents/AgentSkills/README.md" />
-    <Project Path="samples/02-agents/AgentSkills/Agent_Step01_BasicSkills/Agent_Step01_BasicSkills.csproj" />
+    <Project Path="samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/Agent_Step01_FileBasedSkills.csproj" />
+    <Project Path="samples/02-agents/AgentSkills/Agent_Step02_CodeDefinedSkills/Agent_Step02_CodeDefinedSkills.csproj" />
  </Folder>
  <Folder Name="/Samples/02-agents/AGUI/Step05_StateManagement/">
    <Project Path="samples/02-agents/AGUI/Step05_StateManagement/Client/Client.csproj" />
@@ -118,6 +122,34 @@
    <Project Path="samples/02-agents/AgentWithAnthropic/Agent_Anthropic_Step03_UsingFunctionTools/Agent_Anthropic_Step03_UsingFunctionTools.csproj" />
    <Project Path="samples/02-agents/AgentWithAnthropic/Agent_Anthropic_Step04_UsingSkills/Agent_Anthropic_Step04_UsingSkills.csproj" />
  </Folder>
+  <Folder Name="/Samples/02-agents/AgentsWithFoundry/">
+    <File Path="samples/02-agents/AgentsWithFoundry/README.md" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step00_FoundryAgentLifecycle/Agent_Step00_FoundryAgentLifecycle.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step01_Basics/Agent_Step01_Basics.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step02.1_MultiturnConversation/Agent_Step02.1_MultiturnConversation.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step02.2_MultiturnWithServerConversations/Agent_Step02.2_MultiturnWithServerConversations.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step03_UsingFunctionTools/Agent_Step03_UsingFunctionTools.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step04_UsingFunctionToolsWithApprovals/Agent_Step04_UsingFunctionToolsWithApprovals.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step05_StructuredOutput/Agent_Step05_StructuredOutput.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step06_PersistedConversations/Agent_Step06_PersistedConversations.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step07_Observability/Agent_Step07_Observability.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step08_DependencyInjection/Agent_Step08_DependencyInjection.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step09_UsingMcpClientAsTools/Agent_Step09_UsingMcpClientAsTools.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step10_UsingImages/Agent_Step10_UsingImages.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step11_AsFunctionTool/Agent_Step11_AsFunctionTool.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step12_Middleware/Agent_Step12_Middleware.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step13_Plugins/Agent_Step13_Plugins.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step14_CodeInterpreter/Agent_Step14_CodeInterpreter.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step15_ComputerUse/Agent_Step15_ComputerUse.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step16_FileSearch/Agent_Step16_FileSearch.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step17_OpenAPITools/Agent_Step17_OpenAPITools.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step18_BingCustomSearch/Agent_Step18_BingCustomSearch.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step19_SharePoint/Agent_Step19_SharePoint.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step20_MicrosoftFabric/Agent_Step20_MicrosoftFabric.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step21_WebSearch/Agent_Step21_WebSearch.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step22_MemorySearch/Agent_Step22_MemorySearch.csproj" />
+    <Project Path="samples/02-agents/AgentsWithFoundry/Agent_Step23_LocalMCP/Agent_Step23_LocalMCP.csproj" />
+  </Folder>
  <Folder Name="/Samples/02-agents/AgentWithMemory/">
    <File Path="samples/02-agents/AgentWithMemory/README.md" />
    <Project Path="samples/02-agents/AgentWithMemory/AgentWithMemory_Step01_ChatHistoryMemory/AgentWithMemory_Step01_ChatHistoryMemory.csproj" />
@@ -140,35 +172,6 @@
    <Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step03_CustomRAGDataSource/AgentWithRAG_Step03_CustomRAGDataSource.csproj" />
    <Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/AgentWithRAG_Step04_FoundryServiceRAG.csproj" />
  </Folder>
-  <Folder Name="/Samples/02-agents/FoundryAgents/">
-    <File Path="samples/02-agents/FoundryAgents/README.md" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step01_RedTeaming/FoundryAgents_Evaluations_Step01_RedTeaming.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step02_SelfReflection/FoundryAgents_Evaluations_Step02_SelfReflection.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step01.1_Basics/FoundryAgents_Step01.1_Basics.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step01.2_Running/FoundryAgents_Step01.2_Running.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step02_MultiturnConversation/FoundryAgents_Step02_MultiturnConversation.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step03_UsingFunctionTools/FoundryAgents_Step03_UsingFunctionTools.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step04_UsingFunctionToolsWithApprovals/FoundryAgents_Step04_UsingFunctionToolsWithApprovals.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step05_StructuredOutput/FoundryAgents_Step05_StructuredOutput.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step06_PersistedConversations/FoundryAgents_Step06_PersistedConversations.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step07_Observability/FoundryAgents_Step07_Observability.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step08_DependencyInjection/FoundryAgents_Step08_DependencyInjection.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step09_UsingMcpClientAsTools/FoundryAgents_Step09_UsingMcpClientAsTools.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step10_UsingImages/FoundryAgents_Step10_UsingImages.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step11_AsFunctionTool/FoundryAgents_Step11_AsFunctionTool.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step12_Middleware/FoundryAgents_Step12_Middleware.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step13_Plugins/FoundryAgents_Step13_Plugins.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step14_CodeInterpreter/FoundryAgents_Step14_CodeInterpreter.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step15_ComputerUse/FoundryAgents_Step15_ComputerUse.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step16_FileSearch/FoundryAgents_Step16_FileSearch.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step17_OpenAPITools/FoundryAgents_Step17_OpenAPITools.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step18_BingCustomSearch/FoundryAgents_Step18_BingCustomSearch.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step19_SharePoint/FoundryAgents_Step19_SharePoint.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step20_MicrosoftFabric/FoundryAgents_Step20_MicrosoftFabric.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step21_WebSearch/FoundryAgents_Step21_WebSearch.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step22_MemorySearch/FoundryAgents_Step22_MemorySearch.csproj" />
-    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step23_LocalMCP/FoundryAgents_Step23_LocalMCP.csproj" />
-  </Folder>
  <Folder Name="/Samples/02-agents/ModelContextProtocol/">
    <File Path="samples/02-agents/ModelContextProtocol/README.md" />
    <Project Path="samples/02-agents/ModelContextProtocol/Agent_MCP_Server/Agent_MCP_Server.csproj" />
@@ -315,8 +318,8 @@
  <Folder Name="/Samples/05-end-to-end/AspNetAgentAuthorization/">
    <File Path="samples/05-end-to-end/AspNetAgentAuthorization/docker-compose.yml" />
    <File Path="samples/05-end-to-end/AspNetAgentAuthorization/README.md" />
-    <Project Path="samples/05-end-to-end/AspNetAgentAuthorization/Service/Service.csproj" />
    <Project Path="samples/05-end-to-end/AspNetAgentAuthorization/RazorWebClient/RazorWebClient.csproj" />
+    <Project Path="samples/05-end-to-end/AspNetAgentAuthorization/Service/Service.csproj" />
  </Folder>
  <Folder Name="/Solution Items/">
    <File Path=".editorconfig" />
@@ -2,17 +2,19 @@
  <PropertyGroup>
    <!-- Central version prefix - applies to all nuget packages. -->
    <VersionPrefix>1.0.0</VersionPrefix>
-    <RCNumber>4</RCNumber>
+    <RCNumber>5</RCNumber>
    <PackageVersion Condition="'$(IsReleaseCandidate)' == 'true'">$(VersionPrefix)-rc$(RCNumber)</PackageVersion>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260311.1</PackageVersion>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260311.1</PackageVersion>
-    <GitTag>1.0.0-rc4</GitTag>
+    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260330.1</PackageVersion>
+    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260330.1</PackageVersion>
+    <GitTag>1.0.0-rc5</GitTag>

    <Configurations>Debug;Release;Publish</Configurations>
    <IsPackable>true</IsPackable>

    <!-- Package validation. Baseline Version should be the latest version available on NuGet. -->
-    <PackageValidationBaselineVersion>0.0.1</PackageValidationBaselineVersion>
+    <PackageValidationBaselineVersion>1.0.0-rc4</PackageValidationBaselineVersion>
+    <!-- Enable validation for RC packages and GA packages -->
+    <EnablePackageValidation Condition="'$(IsReleaseCandidate)' == 'true' OR '$(IsGenerallyAvailable)' == 'true'">true</EnablePackageValidation>
    <!-- Validate assembly attributes only for Publish builds -->
    <NoWarn Condition="'$(Configuration)' != 'Publish'">$(NoWarn);CP0003</NoWarn>
    <!-- Do not validate reference assemblies -->
@@ -70,7 +70,7 @@ while ((input = Console.ReadLine()) != null && !input.Equals("exit", StringCompa

                        if (approvalRequest.AdditionalProperties != null)
                        {
-                            approvalResponse.AdditionalProperties = new AdditionalPropertiesDictionary();
+                            approvalResponse.AdditionalProperties = [];
                            foreach (var kvp in approvalRequest.AdditionalProperties)
                            {
                                approvalResponse.AdditionalProperties[kvp.Key] = kvp.Value;
@@ -131,9 +131,9 @@ internal sealed class ServerFunctionApprovalClientAgent : DelegatingAIAgent
                    transformedContents ??= CopyContentsUpToIndex(message.Contents, contentIndex);
                    approvalCalls.Remove(functionResult.CallId);
                }
-                else if (transformedContents != null)
+                else
                {
-                    transformedContents.Add(content);
+                    transformedContents?.Add(content);
                }
            }

@@ -155,10 +155,10 @@ internal sealed class ServerFunctionApprovalClientAgent : DelegatingAIAgent
                result ??= CopyMessagesUpToIndex(messages, messageIndex);
                result.Add(newMessage);
            }
-            else if (result != null)
+            else
            {
                // We're already copying messages, so copy this unchanged message too
-                result.Add(message);
+                result?.Add(message);
            }
            // If result is null, we haven't made any changes yet, so keep processing
        }
@@ -57,16 +57,10 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
            throw new InvalidOperationException("Invalid request_approval tool call");
        }

-        var request = toolCall.Arguments.TryGetValue("request", out var reqObj) &&
+        var request = (toolCall.Arguments.TryGetValue("request", out var reqObj) &&
            reqObj is JsonElement argsElement &&
            argsElement.Deserialize(jsonSerializerOptions.GetTypeInfo(typeof(ApprovalRequest))) is ApprovalRequest approvalRequest &&
-            approvalRequest != null ? approvalRequest : null;
-
-        if (request == null)
-        {
-            throw new InvalidOperationException("Failed to deserialize approval request from tool call");
-        }
-
+            approvalRequest != null ? approvalRequest : null) ?? throw new InvalidOperationException("Failed to deserialize approval request from tool call");
        return new ToolApprovalRequestContent(
            requestId: request.ApprovalId,
            new FunctionCallContent(
@@ -77,17 +71,11 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent

    private static ToolApprovalResponseContent ConvertToolResultToApprovalResponse(FunctionResultContent result, ToolApprovalRequestContent approval, JsonSerializerOptions jsonSerializerOptions)
    {
-        var approvalResponse = result.Result is JsonElement je ?
+        var approvalResponse = (result.Result is JsonElement je ?
            (ApprovalResponse?)je.Deserialize(jsonSerializerOptions.GetTypeInfo(typeof(ApprovalResponse))) :
            result.Result is string str ?
                (ApprovalResponse?)JsonSerializer.Deserialize(str, jsonSerializerOptions.GetTypeInfo(typeof(ApprovalResponse))) :
-                result.Result as ApprovalResponse;
-
-        if (approvalResponse == null)
-        {
-            throw new InvalidOperationException("Failed to deserialize approval response from tool result");
-        }
-
+                result.Result as ApprovalResponse) ?? throw new InvalidOperationException("Failed to deserialize approval response from tool result");
        return approval.CreateResponse(approvalResponse.Approved);
    }
 #pragma warning restore MEAI001
@@ -121,7 +109,7 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
        // Track approval ID to original call ID mapping
        _ = new Dictionary<string, string>();
 #pragma warning disable MEAI001 // Type is for evaluation purposes only and is subject to change or removal in future updates. Suppress this diagnostic to proceed.
-        Dictionary<string, ToolApprovalRequestContent> trackedRequestApprovalToolCalls = new(); // Remote approvals
+        Dictionary<string, ToolApprovalRequestContent> trackedRequestApprovalToolCalls = []; // Remote approvals
        for (int messageIndex = 0; messageIndex < messages.Count; messageIndex++)
        {
            var message = messages[messageIndex];
@@ -146,7 +134,7 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
                    });
                }
                else if (content is FunctionResultContent toolResult &&
-                    trackedRequestApprovalToolCalls.TryGetValue(toolResult.CallId, out var approval) == true)
+                    trackedRequestApprovalToolCalls.TryGetValue(toolResult.CallId, out var approval))
                {
                    result ??= CopyMessagesUpToIndex(messages, messageIndex);
                    transformedContents ??= CopyContentsUpToIndex(message.Contents, j);
@@ -161,9 +149,9 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
                        AdditionalProperties = message.AdditionalProperties
                    });
                }
-                else if (result != null)
+                else
                {
-                    result.Add(message);
+                    result?.Add(message);
                }
            }
        }
@@ -72,10 +72,9 @@ internal sealed class StatefulAgent<TState> : DelegatingAIAgent
                if (content is DataContent dataContent && dataContent.MediaType == "application/json")
                {
                    // Deserialize the state
-                    TState? newState = JsonSerializer.Deserialize(
+                    if (JsonSerializer.Deserialize(
                        dataContent.Data.Span,
-                        this._jsonSerializerOptions.GetTypeInfo(typeof(TState))) as TState;
-                    if (newState != null)
+                        this._jsonSerializerOptions.GetTypeInfo(typeof(TState))) is TState newState)
                    {
                        this.State = newState;
                    }
@@ -18,6 +18,7 @@ using OpenTelemetry.Trace;

 #region Setup Telemetry

+// Source name for this sample's custom ActivitySource and Meter; other instrumentation uses their own sources/categories.
 const string SourceName = "OpenTelemetryAspire.ConsoleApp";
 const string ServiceName = "AgentOpenTelemetry";

@@ -40,7 +41,6 @@ var resource = ResourceBuilder.CreateDefault()
 var tracerProviderBuilder = Sdk.CreateTracerProviderBuilder()
    .SetResourceBuilder(ResourceBuilder.CreateDefault().AddService(ServiceName, serviceVersion: "1.0.0"))
    .AddSource(SourceName) // Our custom activity source
-    .AddSource("*Microsoft.Agents.AI") // Agent Framework telemetry
    .AddHttpClientInstrumentation() // Capture HTTP calls to OpenAI
    .AddOtlpExporter(options => options.Endpoint = new Uri(otlpEndpoint));

@@ -54,8 +54,7 @@ using var tracerProvider = tracerProviderBuilder.Build();
 // Setup metrics with resource and instrument name filtering
 using var meterProvider = Sdk.CreateMeterProviderBuilder()
    .SetResourceBuilder(ResourceBuilder.CreateDefault().AddService(ServiceName, serviceVersion: "1.0.0"))
-    .AddMeter(SourceName) // Our custom meter
-    .AddMeter("*Microsoft.Agents.AI") // Agent Framework metrics
+    .AddMeter(SourceName) // Our custom meter source
    .AddHttpClientInstrumentation() // HTTP client metrics
    .AddRuntimeInstrumentation() // .NET runtime metrics
    .AddOtlpExporter(options => options.Endpoint = new Uri(otlpEndpoint))
@@ -128,7 +127,7 @@ var agent = new ChatClientAgent(instrumentedChatClient,
    instructions: "You are a helpful assistant that provides concise and informative responses.",
    tools: [AIFunctionFactory.Create(GetWeatherAsync)])
    .AsBuilder()
-    .UseOpenTelemetry(SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) // enable telemetry at the agent level
+    .UseOpenTelemetry(sourceName: SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) // enable telemetry at the agent level
    .Build();

 var session = await agent.CreateSessionAsync();
@@ -6,6 +6,7 @@ using Azure.AI.Projects;
 using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.AzureAI;

 var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
@@ -30,14 +31,18 @@ var createdAgentVersion = aiProjectClient.Agents.CreateAgentVersion(agentName: J
 //      agentVersion.Name = <agentName>

 // You can use an AIAgent with an already created server side agent version.
-AIAgent existingJokerAgent = aiProjectClient.AsAIAgent(createdAgentVersion);
+FoundryAgent existingJokerAgent = aiProjectClient.AsAIAgent(createdAgentVersion);

 // You can also create another AIAgent version by providing the same name with a different definition.
-AIAgent newJokerAgent = await aiProjectClient.CreateAIAgentAsync(name: JokerName, model: deploymentName, instructions: "You are extremely hilarious at telling jokes.");
+AgentVersion newJokerAgentVersion = await aiProjectClient.Agents.CreateAgentVersionAsync(
+    JokerName,
+    new AgentVersionCreationOptions(new PromptAgentDefinition(model: deploymentName) { Instructions = "You are extremely hilarious at telling jokes." }));
+FoundryAgent newJokerAgent = aiProjectClient.AsAIAgent(newJokerAgentVersion);

 // You can also get the AIAgent latest version just providing its name.
-AIAgent jokerAgentLatest = await aiProjectClient.GetAIAgentAsync(name: JokerName);
-var latestAgentVersion = jokerAgentLatest.GetService<AgentVersion>()!;
+AgentRecord jokerAgentRecord = await aiProjectClient.Agents.GetAgentAsync(JokerName);
+FoundryAgent jokerAgentLatest = aiProjectClient.AsAIAgent(jokerAgentRecord);
+AgentVersion latestAgentVersion = jokerAgentRecord.GetLatestVersion();

 // The AIAgent version can be accessed via the GetService method.
 Console.WriteLine($"Latest agent version id: {latestAgentVersion.Id}");
@@ -1,50 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample demonstrates how to use Agent Skills with a ChatClientAgent.
-// Agent Skills are modular packages of instructions and resources that extend an agent's capabilities.
-// Skills follow the progressive disclosure pattern: advertise -> load -> read resources.
-//
-// This sample includes the expense-report skill:
-//   - Policy-based expense filing with references and assets
-
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using OpenAI.Responses;
-
-// --- Configuration ---
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
-    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// --- Skills Provider ---
-// Discovers skills from the 'skills' directory and makes them available to the agent
-var skillsProvider = new FileAgentSkillsProvider(skillPath: Path.Combine(AppContext.BaseDirectory, "skills"));
-
-// --- Agent Setup ---
-AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetResponsesClient()
-    .AsAIAgent(new ChatClientAgentOptions
-    {
-        Name = "SkillsAgent",
-        ChatOptions = new()
-        {
-            Instructions = "You are a helpful assistant.",
-        },
-        AIContextProviders = [skillsProvider],
-    },
-    model: deploymentName);
-
-// --- Example 1: Expense policy question (loads FAQ resource) ---
-Console.WriteLine("Example 1: Checking expense policy FAQ");
-Console.WriteLine("---------------------------------------");
-AgentResponse response1 = await agent.RunAsync("Are tips reimbursable? I left a 25% tip on a taxi ride and want to know if that's covered.");
-Console.WriteLine($"Agent: {response1.Text}\n");
-
-// --- Example 2: Filing an expense report (multi-turn with template asset) ---
-Console.WriteLine("Example 2: Filing an expense report");
-Console.WriteLine("---------------------------------------");
-AgentSession session = await agent.CreateSessionAsync();
-AgentResponse response2 = await agent.RunAsync("I had 3 client dinners and a $1,200 flight last week. Return a draft expense report and ask about any missing details.",
-    session);
-Console.WriteLine($"Agent: {response2.Text}\n");
@@ -1,63 +0,0 @@
-# Agent Skills Sample
-
-This sample demonstrates how to use **Agent Skills** with a `ChatClientAgent` in the Microsoft Agent Framework.
-
-## What are Agent Skills?
-
-Agent Skills are modular packages of instructions and resources that enable AI agents to perform specialized tasks. They follow the [Agent Skills specification](https://agentskills.io/) and implement the progressive disclosure pattern:
-
-1. **Advertise**: Skills are advertised with name + description (~100 tokens per skill)
-2. **Load**: Full instructions are loaded on-demand via `load_skill` tool
-3. **Resources**: References and other files loaded via `read_skill_resource` tool
-
-## Skills Included
-
-### expense-report
-Policy-based expense filing with spending limits, receipt requirements, and approval workflows.
- `references/POLICY_FAQ.md` — Detailed expense policy Q&A
- `assets/expense-report-template.md` — Submission template
-
-## Project Structure
-
-```
-Agent_Step01_BasicSkills/
-├── Program.cs
-├── Agent_Step01_BasicSkills.csproj
-└── skills/
-    └── expense-report/
-        ├── SKILL.md
-        ├── references/
-        │   └── POLICY_FAQ.md
-        └── assets/
-            └── expense-report-template.md
-```
-
-## Running the Sample
-
-### Prerequisites
- .NET 10.0 SDK
- Azure OpenAI endpoint with a deployed model
-
-### Setup
-1. Set environment variables:
-   ```bash
-   export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
-   export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
-   ```
-
-2. Run the sample:
-   ```bash
-   dotnet run
-   ```
-
-### Examples
-
-The sample runs two examples:
-
-1. **Expense policy FAQ** — Asks about tip reimbursement; the agent loads the expense-report skill and reads the FAQ resource
-2. **Filing an expense report** — Multi-turn conversation to draft an expense report using the template asset
-
-## Learn More
-
- [Agent Skills Specification](https://agentskills.io/)
- [Microsoft Agent Framework Documentation](../../../../../docs/)
@@ -1,40 +0,0 @@
---
-name: expense-report
-description: File and validate employee expense reports according to Contoso company policy. Use when asked about expense submissions, reimbursement rules, receipt requirements, spending limits, or expense categories.
-metadata:
-  author: contoso-finance
-  version: "2.1"
---
-
-# Expense Report
-
-## Categories and Limits
-
-| Category | Limit | Receipt | Approval |
-|---|---|---|---|
-| Meals — solo | $50/day | >$25 | No |
-| Meals — team/client | $75/person | Always | Manager if >$200 total |
-| Lodging | $250/night | Always | Manager if >3 nights |
-| Ground transport | $100/day | >$15 | No |
-| Airfare | Economy | Always | Manager; VP if >$1,500 |
-| Conference/training | $2,000/event | Always | Manager + L&D |
-| Office supplies | $100 | Yes | No |
-| Software/subscriptions | $50/month | Yes | Manager if >$200/year |
-
-## Filing Process
-
-1. Collect receipts — must show vendor, date, amount, payment method.
-2. Categorize per table above.
-3. Use template: [assets/expense-report-template.md](assets/expense-report-template.md).
-4. For client/team meals: list attendee names and business purpose.
-5. Submit — auto-approved if <$500; manager if $500–$2,000; VP if >$2,000.
-6. Reimbursement: 10 business days via direct deposit.
-
-## Policy Rules
-
- Submit within 30 days of transaction.
- Alcohol is never reimbursable.
- Foreign currency: convert to USD at transaction-date rate; note original currency and amount.
- Mixed personal/business travel: only business portion reimbursable; provide comparison quotes.
- Lost receipts (>$25): file Lost Receipt Affidavit from Finance. Max 2 per quarter.
- For policy questions not covered above, consult the FAQ: [references/POLICY_FAQ.md](references/POLICY_FAQ.md). Answers should be based on what this document and the FAQ state.
@@ -1,5 +0,0 @@
-# Expense Report Template
-
-| Date | Category | Vendor | Description | Amount (USD) | Original Currency | Original Amount | Attendees | Business Purpose | Receipt Attached |
-|------|----------|--------|-------------|--------------|-------------------|-----------------|-----------|------------------|------------------|
-|      |          |        |             |              |                   |                 |           |                  | Yes or No        |
@@ -1,55 +0,0 @@
-# Expense Policy — Frequently Asked Questions
-
-## Meals
-
-**Q: Can I expense coffee or snacks during the workday?**
-A: Daily coffee/snacks under $10 are not reimbursable (considered personal). Coffee purchased during a client meeting or team working session is reimbursable as a team meal.
-
-**Q: What if a team dinner exceeds the per-person limit?**
-A: The $75/person limit applies as a guideline. Overages up to 20% are accepted with a written justification (e.g., "client dinner at venue chosen by client"). Overages beyond 20% require pre-approval from your VP.
-
-**Q: Do I need to list every attendee?**
-A: Yes. For client meals, list the client's name and company. For team meals, list all employee names. For groups over 10, you may attach a separate attendee list.
-
-## Travel
-
-**Q: Can I book a premium economy or business class flight?**
-A: Economy class is the standard. Premium economy is allowed for flights over 6 hours. Business class requires VP pre-approval and is generally reserved for flights over 10 hours or medical accommodation.
-
-**Q: What about ride-sharing (Uber/Lyft) vs. rental cars?**
-A: Use ride-sharing for trips under 30 miles round-trip. Rent a car for multi-day travel or when ride-sharing would exceed $100/day. Always choose the compact/standard category unless traveling with 3+ people.
-
-**Q: Are tips reimbursable?**
-A: Tips up to 20% are reimbursable for meals, taxi/ride-share, and hotel housekeeping. Tips above 20% require justification.
-
-## Lodging
-
-**Q: What if the $250/night limit isn't enough for the city I'm visiting?**
-A: For high-cost cities (New York, San Francisco, London, Tokyo, Sydney), the limit is automatically increased to $350/night. No additional approval is needed. For other locations where rates are unusually high (e.g., during a major conference), request a per-trip exception from your manager before booking.
-
-**Q: Can I stay with friends/family instead and get a per-diem?**
-A: No. Contoso reimburses actual lodging costs only, not per-diems.
-
-## Subscriptions and Software
-
-**Q: Can I expense a personal productivity tool?**
-A: Software must be directly related to your job function. Tools like IDE licenses, design software, or project management apps are reimbursable. General productivity apps (note-taking, personal calendar) are not, unless your manager confirms a business need in writing.
-
-**Q: What about annual subscriptions?**
-A: Annual subscriptions over $200 require manager approval before purchase. Submit the approval email with your expense report.
-
-## Receipts and Documentation
-
-**Q: My receipt is faded/damaged. What do I do?**
-A: Try to obtain a duplicate from the vendor. If not possible, submit a Lost Receipt Affidavit (available from the Finance SharePoint site). You're limited to 2 affidavits per quarter.
-
-**Q: Do I need a receipt for parking meters or tolls?**
-A: For amounts under $15, no receipt is required — just note the date, location, and amount. For $15 and above, a receipt or bank/credit card statement excerpt is required.
-
-## Approval and Reimbursement
-
-**Q: My manager is on leave. Who approves my report?**
-A: Expense reports can be approved by your skip-level manager or any manager designated as an alternate approver in the expense system.
-
-**Q: Can I submit expenses from a previous quarter?**
-A: The standard 30-day window applies. Expenses older than 30 days require a written explanation and VP approval. Expenses older than 90 days are not reimbursable except in extraordinary circumstances (extended leave, medical emergency) with CFO approval.
@@ -14,6 +14,10 @@
    <PackageReference Include="Azure.Identity" />
  </ItemGroup>

+  <ItemGroup>
+    <Compile Include="..\SubprocessScriptRunner.cs" Link="SubprocessScriptRunner.cs" />
+  </ItemGroup>
+
  <ItemGroup>
    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
  </ItemGroup>
@@ -0,0 +1,48 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use file-based Agent Skills with a ChatClientAgent.
+// Skills are discovered from SKILL.md files on disk and follow the progressive disclosure pattern:
+// 1. Advertise — skill names and descriptions in the system prompt
+// 2. Load — full instructions loaded on demand via load_skill tool
+// 3. Read resources — reference files read via read_skill_resource tool
+// 4. Run scripts — scripts executed via run_skill_script tool with a subprocess executor
+//
+// This sample uses a unit-converter skill that converts between miles, kilometers, pounds, and kilograms.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using OpenAI.Responses;
+
+// --- Configuration ---
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// --- Skills Provider ---
+// Discovers skills from the 'skills' directory containing SKILL.md files.
+// The script runner runs file-based scripts (e.g. Python) as local subprocesses.
+var skillsProvider = new AgentSkillsProvider(
+    Path.Combine(AppContext.BaseDirectory, "skills"),
+    SubprocessScriptRunner.RunAsync);
+// --- Agent Setup ---
+AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
+    .GetResponsesClient()
+    .AsAIAgent(new ChatClientAgentOptions
+    {
+        Name = "UnitConverterAgent",
+        ChatOptions = new()
+        {
+            Instructions = "You are a helpful assistant that can convert units.",
+        },
+        AIContextProviders = [skillsProvider],
+    },
+    model: deploymentName);
+
+// --- Example: Unit conversion ---
+Console.WriteLine("Converting units with file-based skills");
+Console.WriteLine(new string('-', 60));
+
+AgentResponse response = await agent.RunAsync(
+    "How many kilometers is a marathon (26.2 miles)? And how many pounds is 75 kilograms?");
+
+Console.WriteLine($"Agent: {response.Text}");
@@ -0,0 +1,51 @@
+# File-Based Agent Skills Sample
+
+This sample demonstrates how to use **file-based Agent Skills** with a `ChatClientAgent`.
+
+## What it demonstrates
+
+- Discovering skills from `SKILL.md` files on disk via `AgentFileSkillsSource`
+- The progressive disclosure pattern: advertise → load → read resources → run scripts
+- Using the `AgentSkillsProvider` constructor with a skill directory path and script executor
+- Running file-based scripts (Python) via a subprocess-based executor
+
+## Skills Included
+
+### unit-converter
+
+Converts between common units (miles↔km, pounds↔kg) using a multiplication factor.
+
+- `references/conversion-table.md` — Conversion factor table
+- `scripts/convert.py` — Python script that performs the conversion
+
+## Running the Sample
+
+### Prerequisites
+
+- .NET 10.0 SDK
+- Azure OpenAI endpoint with a deployed model
+- Python 3 installed and available as `python3` on your PATH
+
+### Setup
+
+```bash
+export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
+export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+### Run
+
+```bash
+dotnet run
+```
+
+### Expected Output
+
+```
+Converting units with file-based skills
+------------------------------------------------------------
+Agent: Here are your conversions:
+
+1. **26.2 miles → 42.16 km** (a marathon distance)
+2. **75 kg → 165.35 lbs**
+```
@@ -0,0 +1,11 @@
+---
+name: unit-converter
+description: Convert between common units using a multiplication factor. Use when asked to convert miles, kilometers, pounds, or kilograms.
+---
+
+## Usage
+
+When the user requests a unit conversion:
+1. First, review `references/conversion-table.md` to find the correct factor
+2. Run the `scripts/convert.py` script with `--value <number> --factor <factor>` (e.g. `--value 26.2 --factor 1.60934`)
+3. Present the converted value clearly with both units
@@ -0,0 +1,10 @@
+# Conversion Tables
+
+Formula: **result = value × factor**
+
+| From        | To          | Factor   |
+|-------------|-------------|----------|
+| miles       | kilometers  | 1.60934  |
+| kilometers  | miles       | 0.621371 |
+| pounds      | kilograms   | 0.453592 |
+| kilograms   | pounds      | 2.20462  |
@@ -0,0 +1,29 @@
+# Unit conversion script
+# Converts a value using a multiplication factor: result = value × factor
+#
+# Usage:
+#   python scripts/convert.py --value 26.2 --factor 1.60934
+#   python scripts/convert.py --value 75 --factor 2.20462
+
+import argparse
+import json
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Convert a value using a multiplication factor.",
+        epilog="Examples:\n"
+        "  python scripts/convert.py --value 26.2 --factor 1.60934\n"
+        "  python scripts/convert.py --value 75 --factor 2.20462",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument("--value", type=float, required=True, help="The numeric value to convert.")
+    parser.add_argument("--factor", type=float, required=True, help="The conversion factor from the table.")
+    args = parser.parse_args()
+
+    result = round(args.value * args.factor, 4)
+    print(json.dumps({"value": args.value, "factor": args.factor, "result": result}))
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,21 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <NoWarn>$(NoWarn);MAAI001</NoWarn>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,90 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to define Agent Skills entirely in code using AgentInlineSkill.
+// No SKILL.md files are needed — skills, resources, and scripts are all defined programmatically.
+//
+// Three approaches are shown using a unit-converter skill:
+// 1. Static resources — inline content provided via AddResource
+// 2. Dynamic resources — computed at runtime via a factory delegate
+// 3. Code scripts — executable delegates the agent can invoke directly
+
+using System.Text.Json;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using OpenAI.Responses;
+
+// --- Configuration ---
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// --- Build the code-defined skill ---
+var unitConverterSkill = new AgentInlineSkill(
+    name: "unit-converter",
+    description: "Convert between common units using a multiplication factor. Use when asked to convert miles, kilometers, pounds, or kilograms.",
+    instructions: """
+        Use this skill when the user asks to convert between units.
+
+        1. Review the conversion-table resource to find the factor for the requested conversion.
+        2. Check the conversion-policy resource for rounding and formatting rules.
+        3. Use the convert script, passing the value and factor from the table.
+        """)
+    // 1. Static Resource: conversion tables
+    .AddResource(
+        "conversion-table",
+        """
+        # Conversion Tables
+
+        Formula: **result = value × factor**
+
+        | From        | To          | Factor   |
+        |-------------|-------------|----------|
+        | miles       | kilometers  | 1.60934  |
+        | kilometers  | miles       | 0.621371 |
+        | pounds      | kilograms   | 0.453592 |
+        | kilograms   | pounds      | 2.20462  |
+        """)
+    // 2. Dynamic Resource: conversion policy (computed at runtime)
+    .AddResource("conversion-policy", () =>
+    {
+        const int Precision = 4;
+        return $"""
+            # Conversion Policy
+
+            **Decimal places:** {Precision}
+            **Format:** Always show both the original and converted values with units
+            **Generated at:** {DateTime.UtcNow:O}
+            """;
+    })
+    // 3. Code Script: convert
+    .AddScript("convert", (double value, double factor) =>
+    {
+        double result = Math.Round(value * factor, 4);
+        return JsonSerializer.Serialize(new { value, factor, result });
+    });
+
+// --- Skills Provider ---
+var skillsProvider = new AgentSkillsProvider(unitConverterSkill);
+
+// --- Agent Setup ---
+AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
+    .GetResponsesClient()
+    .AsAIAgent(new ChatClientAgentOptions
+    {
+        Name = "UnitConverterAgent",
+        ChatOptions = new()
+        {
+            Instructions = "You are a helpful assistant that can convert units.",
+        },
+        AIContextProviders = [skillsProvider],
+    },
+    model: deploymentName);
+
+// --- Example: Unit conversion ---
+Console.WriteLine("Converting units with code-defined skills");
+Console.WriteLine(new string('-', 60));
+
+AgentResponse response = await agent.RunAsync(
+    "How many kilometers is a marathon (26.2 miles)? And how many pounds is 75 kilograms?");
+
+Console.WriteLine($"Agent: {response.Text}");
@@ -0,0 +1,52 @@
+# Code-Defined Agent Skills Sample
+
+This sample demonstrates how to define **Agent Skills entirely in code** using `AgentInlineSkill`.
+
+## What it demonstrates
+
+- Creating skills programmatically with `AgentInlineSkill` — no SKILL.md files needed
+- **Static resources** via `AddResource` with inline content
+- **Dynamic resources** via `AddResource` with a factory delegate (computed at runtime)
+- **Code scripts** via `AddScript` with a delegate handler
+- Using the `AgentSkillsProvider` constructor with inline skills
+
+## Skills Included
+
+### unit-converter (code-defined)
+
+Converts between common units using multiplication factors. Defined entirely in C# code:
+
+- `conversion-table` — Static resource with factor table
+- `conversion-policy` — Dynamic resource with formatting rules (generated at runtime)
+- `convert` — Script that performs `value × factor` conversion
+
+## Running the Sample
+
+### Prerequisites
+
+- .NET 10.0 SDK
+- Azure OpenAI endpoint with a deployed model
+
+### Setup
+
+```bash
+export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
+export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+### Run
+
+```bash
+dotnet run
+```
+
+### Expected Output
+
+```
+Converting units with code-defined skills
+------------------------------------------------------------
+Agent: Here are your conversions:
+
+1. **26.2 miles → 42.16 km** (a marathon distance)
+2. **75 kg → 165.35 lbs**
+```
@@ -1,7 +1,24 @@
 # AgentSkills Samples

-Samples demonstrating Agent Skills capabilities.
+Samples demonstrating Agent Skills capabilities. Each sample shows a different way to define and use skills.

 | Sample | Description |
 |--------|-------------|
-| [Agent_Step01_BasicSkills](Agent_Step01_BasicSkills/) | Using Agent Skills with a ChatClientAgent, including progressive disclosure and skill resources |
+| [Agent_Step01_FileBasedSkills](Agent_Step01_FileBasedSkills/) | Define skills as `SKILL.md` files on disk with reference documents. Uses a unit-converter skill. |
+| [Agent_Step02_CodeDefinedSkills](Agent_Step02_CodeDefinedSkills/) | Define skills entirely in C# code using `AgentInlineSkill`, with static/dynamic resources and scripts. |
+
+## Key Concepts
+
+### File-Based vs Code-Defined Skills
+
+| Aspect | File-Based | Code-Defined |
+|--------|-----------|--------------|
+| Definition | `SKILL.md` files on disk | `AgentInlineSkill` instances in C# |
+| Resources | All files in skill directory (filtered by extension) | `AddResource` (static value or delegate-backed) |
+| Scripts | Supported via script executor delegate | `AddScript` delegates |
+| Discovery | Automatic from directory path | Explicit via constructor |
+| Dynamic content | No (static files only) | Yes (factory delegates) |
+| Reusability | Copy skill directory | Inline or shared instances |
+
+For single-source scenarios, use the `AgentSkillsProvider` constructors directly. To combine multiple skill types, use the `AgentSkillsProviderBuilder`.
+
@@ -0,0 +1,137 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Sample subprocess-based skill script runner.
+// Executes file-based skill scripts as local subprocesses.
+// This is provided for demonstration purposes only.
+
+using System.Diagnostics;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+/// <summary>
+/// Executes file-based skill scripts as local subprocesses.
+/// </summary>
+/// <remarks>
+/// This runner uses the script's absolute path, converts the arguments
+/// to CLI flags, and returns captured output. It is intended for
+/// demonstration purposes only.
+/// </remarks>
+internal static class SubprocessScriptRunner
+{
+    /// <summary>
+    /// Runs a skill script as a local subprocess.
+    /// </summary>
+    public static async Task<object?> RunAsync(
+        AgentFileSkill skill,
+        AgentFileSkillScript script,
+        AIFunctionArguments arguments,
+        CancellationToken cancellationToken)
+    {
+        if (!File.Exists(script.FullPath))
+        {
+            return $"Error: Script file not found: {script.FullPath}";
+        }
+
+        string extension = Path.GetExtension(script.FullPath);
+        string? interpreter = extension switch
+        {
+            ".py" => "python3",
+            ".js" => "node",
+            ".sh" => "bash",
+            ".ps1" => "pwsh",
+            _ => null,
+        };
+
+        var startInfo = new ProcessStartInfo
+        {
+            RedirectStandardOutput = true,
+            RedirectStandardError = true,
+            UseShellExecute = false,
+            CreateNoWindow = true,
+            WorkingDirectory = Path.GetDirectoryName(script.FullPath) ?? ".",
+        };
+
+        if (interpreter is not null)
+        {
+            startInfo.FileName = interpreter;
+            startInfo.ArgumentList.Add(script.FullPath);
+        }
+        else
+        {
+            startInfo.FileName = script.FullPath;
+        }
+
+        if (arguments is not null)
+        {
+            foreach (var (key, value) in arguments)
+            {
+                if (value is bool boolValue)
+                {
+                    if (boolValue)
+                    {
+                        startInfo.ArgumentList.Add(NormalizeKey(key));
+                    }
+                }
+                else if (value is not null)
+                {
+                    startInfo.ArgumentList.Add(NormalizeKey(key));
+                    startInfo.ArgumentList.Add(value.ToString()!);
+                }
+            }
+        }
+
+        Process? process = null;
+        try
+        {
+            process = Process.Start(startInfo);
+            if (process is null)
+            {
+                return $"Error: Failed to start process for script '{script.Name}'.";
+            }
+
+            Task<string> outputTask = process.StandardOutput.ReadToEndAsync(cancellationToken);
+            Task<string> errorTask = process.StandardError.ReadToEndAsync(cancellationToken);
+
+            await process.WaitForExitAsync(cancellationToken).ConfigureAwait(false);
+
+            string output = await outputTask.ConfigureAwait(false);
+            string error = await errorTask.ConfigureAwait(false);
+
+            if (!string.IsNullOrEmpty(error))
+            {
+                output += $"\nStderr:\n{error}";
+            }
+
+            if (process.ExitCode != 0)
+            {
+                output += $"\nScript exited with code {process.ExitCode}";
+            }
+
+            return string.IsNullOrEmpty(output) ? "(no output)" : output.Trim();
+        }
+        catch (OperationCanceledException) when (cancellationToken.IsCancellationRequested)
+        {
+            // Kill the process on cancellation to avoid leaving orphaned subprocesses.
+            process?.Kill(entireProcessTree: true);
+            throw;
+        }
+        catch (OperationCanceledException)
+        {
+            throw;
+        }
+        catch (Exception ex)
+        {
+            return $"Error: Failed to execute script '{script.Name}': {ex.Message}";
+        }
+        finally
+        {
+            process?.Dispose();
+        }
+    }
+
+    /// <summary>
+    /// Normalizes a parameter key to a consistent --flag format.
+    /// Models may return keys with or without leading dashes (e.g., "value" vs "--value").
+    /// </summary>
+    private static string NormalizeKey(string key) => "--" + key.TrimStart('-');
+}
@@ -5,20 +5,13 @@
 using Anthropic;
 using Anthropic.Core;
 using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;

 var apiKey = Environment.GetEnvironmentVariable("ANTHROPIC_API_KEY") ?? throw new InvalidOperationException("ANTHROPIC_API_KEY is not set.");
 var model = Environment.GetEnvironmentVariable("ANTHROPIC_CHAT_MODEL_NAME") ?? "claude-haiku-4-5";

-AIAgent agent = new AnthropicClient(new ClientOptions { ApiKey = apiKey })
+AIAgent agent =
+    new AnthropicClient(new ClientOptions { ApiKey = apiKey })
    .AsAIAgent(model: model, instructions: "You are good at telling jokes.", name: "Joker");

 // Invoke the agent and output the text result.
-var response = await agent.RunAsync("Tell me a joke about a pirate.");
-Console.WriteLine(response);
-
-// Invoke the agent with streaming support.
-await foreach (var update in agent.RunStreamingAsync("Tell me a joke about a pirate."))
-{
-    Console.WriteLine(update);
-}
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
@@ -11,6 +11,7 @@ using System.Text.Json;
 using Azure.AI.Projects;
 using Azure.Identity;
 using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.AzureAI;
 using Microsoft.Agents.AI.FoundryMemory;

 string foundryEndpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
@@ -19,6 +20,9 @@ string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLO
 string embeddingModelName = Environment.GetEnvironmentVariable("AZURE_AI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-ada-002";

 // Create an AIProjectClient for Foundry with Azure Identity authentication.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 DefaultAzureCredential credential = new();
 AIProjectClient projectClient = new(new Uri(foundryEndpoint), credential);

@@ -33,11 +37,15 @@ FoundryMemoryProvider memoryProvider = new(
    memoryStoreName,
    stateInitializer: _ => new(new FoundryMemoryProviderScope("sample-user-123")));

-AIAgent agent = await projectClient.CreateAIAgentAsync(deploymentName,
-    options: new ChatClientAgentOptions()
+FoundryAgent agent = projectClient.AsAIAgent(
+    new ChatClientAgentOptions()
    {
        Name = "TravelAssistantWithFoundryMemory",
-        ChatOptions = new() { Instructions = "You are a friendly travel assistant. Use known memories about the user when responding, and do not invent details." },
+        ChatOptions = new()
+        {
+            ModelId = deploymentName,
+            Instructions = "You are a friendly travel assistant. Use known memories about the user when responding, and do not invent details."
+        },
        AIContextProviders = [memoryProvider]
    });

@@ -1,4 +1,4 @@
-# Agent Framework Retrieval Augmented Generation (RAG)
+# Agent Framework Retrieval Augmented Generation (RAG)

 These samples show how to create an agent with the Agent Framework that uses Memory to remember previous conversations or facts from previous conversations.

@@ -10,4 +10,4 @@ These samples show how to create an agent with the Agent Framework that uses Mem
 |[Memory with Azure AI Foundry](./AgentWithMemory_Step04_MemoryUsingFoundry/)|This sample demonstrates how to create and run an agent that uses Azure AI Foundry's managed memory service to extract and retrieve individual memories.|
 |[Bounded Chat History with Overflow](./AgentWithMemory_Step05_BoundedChatHistory/)|This sample demonstrates how to create a bounded chat history provider that overflows older messages to a vector store and recalls them as memories.|

-> **See also**: [Memory Search with Foundry Agents](../FoundryAgents/FoundryAgents_Step22_MemorySearch/) - demonstrates using the built-in Memory Search tool with Azure Foundry Agents.
+> **See also**: [Memory Search with Foundry Agents](../AgentsWithFoundry/Agent_Step22_MemorySearch/) - demonstrates using the built-in Memory Search tool with Azure Foundry agents.
@@ -4,28 +4,14 @@

 using System.ClientModel;
 using Microsoft.Agents.AI;
-using OpenAI;
-using OpenAI.Chat;
+using OpenAI.Responses;

 var apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY") ?? throw new InvalidOperationException("OPENAI_API_KEY is not set.");
 var model = Environment.GetEnvironmentVariable("OPENAI_CHAT_MODEL_NAME") ?? "gpt-4o-mini";

-AIAgent agent = new OpenAIClient(apiKey)
-    .GetChatClient(model)
-    .AsAIAgent(instructions: "You are good at telling jokes.", name: "Joker");
+AIAgent agent =
+    new ResponsesClient(new ApiKeyCredential(apiKey))
+    .AsAIAgent(model: model, instructions: "You are good at telling jokes.", name: "Joker");

-UserChatMessage chatMessage = new("Tell me a joke about a pirate.");
-
-// Invoke the agent and output the text result.
-ChatCompletion chatCompletion = await agent.RunAsync([chatMessage]);
-Console.WriteLine(chatCompletion.Content.Last().Text);
-
-// Invoke the agent with streaming support.
-AsyncCollectionResult<StreamingChatCompletionUpdate> completionUpdates = agent.RunStreamingAsync([chatMessage]);
-await foreach (StreamingChatCompletionUpdate completionUpdate in completionUpdates)
-{
-    if (completionUpdate.ContentUpdate.Count > 0)
-    {
-        Console.WriteLine(completionUpdate.ContentUpdate[0].Text);
-    }
-}
+// Once you have the agent, you can invoke it like any other AIAgent.
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
@@ -73,16 +73,28 @@ foreach (ClientResult result in getConversationItemsResults.GetRawPages())
    using JsonDocument getConversationItemsResultAsJson = JsonDocument.Parse(result.GetRawResponse().Content.ToString());
    foreach (JsonElement element in getConversationItemsResultAsJson.RootElement.GetProperty("data").EnumerateArray())
    {
+        // Skip non-message items (e.g. tool calls, reasoning) that lack a "role" property
+        if (!element.TryGetProperty("role"u8, out var roleElement))
+        {
+            continue;
+        }
+
        string messageId = element.GetProperty("id"u8).ToString();
-        string messageRole = element.GetProperty("role"u8).ToString();
+        string messageRole = roleElement.ToString();
        Console.WriteLine($"    Message ID: {messageId}");
        Console.WriteLine($"    Message Role: {messageRole}");

-        foreach (var content in element.GetProperty("content").EnumerateArray())
+        if (element.TryGetProperty("content"u8, out var contentElement))
        {
-            string messageContentText = content.GetProperty("text"u8).ToString();
-            Console.WriteLine($"    Message Text: {messageContentText}");
+            foreach (var content in contentElement.EnumerateArray())
+            {
+                if (content.TryGetProperty("text"u8, out var textElement))
+                {
+                    Console.WriteLine($"    Message Text: {textElement}");
+                }
+            }
        }
+
        Console.WriteLine();
    }
 }
@@ -16,7 +16,7 @@ using Qdrant.Client;
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
 var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
 var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
-var afOverviewUrl = "https://github.com/MicrosoftDocs/semantic-kernel-docs/blob/main/agent-framework/overview/agent-framework-overview.md";
+var afOverviewUrl = "https://raw.githubusercontent.com/MicrosoftDocs/semantic-kernel-docs/refs/heads/main/agent-framework/overview/index.md";
 var afMigrationUrl = "https://raw.githubusercontent.com/MicrosoftDocs/semantic-kernel-docs/refs/heads/main/agent-framework/migration-guide/from-semantic-kernel/index.md";

 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
@@ -4,11 +4,13 @@

 using System.ClientModel;
 using Azure.AI.Projects;
+using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
+using Microsoft.Agents.AI.AzureAI;
 using OpenAI;
 using OpenAI.Files;
+using OpenAI.Responses;
 using OpenAI.VectorStores;

 var endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
@@ -37,14 +39,20 @@ ClientResult<VectorStore> vectorStoreCreate = await vectorStoreClient.CreateVect
    FileIds = { uploadResult.Value.Id }
 });

-var fileSearchTool = new HostedFileSearchTool() { Inputs = [new HostedVectorStoreContent(vectorStoreCreate.Value.Id)] };
+// Use the native OpenAI SDK FileSearchTool directly with the vector store ID.
+#pragma warning disable OPENAI001
+FileSearchTool fileSearchTool = new([vectorStoreCreate.Value.Id]);
+#pragma warning restore OPENAI001

-AIAgent agent = await aiProjectClient
-    .CreateAIAgentAsync(
-        model: deploymentName,
-        name: "AskContoso",
-        instructions: "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.",
-        tools: [fileSearchTool]);
+AgentVersion agentVersion = await aiProjectClient.Agents.CreateAgentVersionAsync(
+    "AskContoso",
+    new AgentVersionCreationOptions(
+        new PromptAgentDefinition(model: deploymentName)
+        {
+            Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available.",
+            Tools = { fileSearchTool }
+        }));
+FoundryAgent agent = aiProjectClient.AsAIAgent(agentVersion);

 AgentSession session = await agent.CreateSessionAsync();

@@ -3,6 +3,7 @@
 // This sample shows how to expose an AI agent as an MCP tool.

 using Azure.AI.Projects;
+using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.DependencyInjection;
@@ -18,11 +19,17 @@ var deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYME
 var aiProjectClient = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential());

 // Create a server side agent and expose it as an AIAgent.
-AIAgent agent = await aiProjectClient.CreateAIAgentAsync(
-    model: deploymentName,
-    instructions: "You are good at telling jokes, and you always start each joke with 'Aye aye, captain!'.",
-    name: "Joker",
-    description: "An agent that tells jokes.");
+AgentVersion agentVersion = await aiProjectClient.Agents.CreateAgentVersionAsync(
+    "Joker",
+    new AgentVersionCreationOptions(
+        new PromptAgentDefinition(model: deploymentName)
+        {
+            Instructions = "You are good at telling jokes, and you always start each joke with 'Aye aye, captain!'.",
+        })
+    {
+        Description = "An agent that tells jokes.",
+    });
+AIAgent agent = aiProjectClient.AsAIAgent(agentVersion);

 // Convert the agent to an AIFunction and then to an MCP tool.
 // The agent name and description will be used as the mcp tool name and description.
@@ -16,5 +16,11 @@
  <ItemGroup>
    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
  </ItemGroup>
+
+  <ItemGroup>
+    <None Update="Assets\walkway.jpg">
+      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
+    </None>
+  </ItemGroup>
  
 </Project>
@@ -22,7 +22,7 @@ var agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential(

 ChatMessage message = new(ChatRole.User, [
    new TextContent("What do you see in this image?"),
-    new UriContent("https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg", "image/jpeg")
+    await DataContent.LoadFromAsync("Assets/walkway.jpg"),
 ]);

 var session = await agent.CreateSessionAsync();
@@ -189,9 +189,9 @@ async Task<AgentResponse> PIIMiddleware(IEnumerable<ChatMessage> messages, Agent
        // Regex patterns for PII detection (simplified for demonstration)
        Regex[] piiPatterns =
        [
-            new(@"\b\d{3}-\d{3}-\d{4}\b", RegexOptions.Compiled), // Phone number (e.g., 123-456-7890)
-            new(@"\b[\w\.-]+@[\w\.-]+\.\w+\b", RegexOptions.Compiled), // Email address
-            new(@"\b[A-Z][a-z]+\s[A-Z][a-z]+\b", RegexOptions.Compiled) // Full name (e.g., John Doe)
+            MyRegex(), // Phone number (e.g., 123-456-7890)
+            EmailRegex(), // Email address
+            FullNameRegex() // Full name (e.g., John Doe)
        ];

        foreach (var pattern in piiPatterns)
@@ -309,3 +309,15 @@ internal sealed class DateTimeContextProvider : MessageAIContextProvider
            ]);
    }
 }
+
+internal partial class Program
+{
+    [GeneratedRegex(@"\b\d{3}-\d{3}-\d{4}\b", RegexOptions.Compiled)]
+    private static partial Regex MyRegex();
+
+    [GeneratedRegex(@"\b[\w\.-]+@[\w\.-]+\.\w+\b", RegexOptions.Compiled)]
+    private static partial Regex EmailRegex();
+
+    [GeneratedRegex(@"\b[A-Z][a-z]+\s[A-Z][a-z]+\b", RegexOptions.Compiled)]
+    private static partial Regex FullNameRegex();
+}
@@ -17,10 +17,10 @@ var bingConnectionId = Environment.GetEnvironmentVariable("AZURE_AI_BING_CONNECT
 PersistentAgentsAdministrationClientOptions persistentAgentsClientOptions = new();
 persistentAgentsClientOptions.Retry.NetworkTimeout = TimeSpan.FromMinutes(20);

-// Get a client to create/retrieve server side agents with.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+// Get a client to create/retrieve server side agents with.
 PersistentAgentsClient persistentAgentsClient = new(endpoint, new DefaultAzureCredential(), persistentAgentsClientOptions);

 // Define and configure the Deep Research tool.
@@ -23,12 +23,14 @@ Before running this sample, ensure you have:

 Pay special attention to the purple `Note` boxes in the Azure documentation.

-**Note**: The Bing Connection ID must be from the **project**, not the resource. It has the following format:
+**Note**: The Bing Grounding Connection ID must be the **full ARM resource URI** from the project, not just the connection name. It has the following format:

 ```
-/subscriptions/<sub_id>/resourceGroups/<rg_name>/providers/<provider_name>/accounts/<account_name>/projects/<project_name>/connections/<connection_name>
+/subscriptions/<sub-id>/resourceGroups/<rg>/providers/Microsoft.CognitiveServices/accounts/<account>/projects/<project>/connections/<connection-name>
 ```

+You can find this in the Azure AI Foundry portal under **Management > Connected resources**, or retrieve it programmatically via the connections API (`.id` property).
+
 ## Environment Variables

 Set the following environment variables:
@@ -37,8 +39,8 @@ Set the following environment variables:
 # Replace with your Azure AI Foundry project endpoint
 $env:AZURE_AI_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/"

-# Replace with your Bing connection ID from the project
-$env:AZURE_AI_BING_CONNECTION_ID="/subscriptions/.../connections/your-bing-connection"
+# Replace with your Bing Grounding connection ID (full ARM resource URI)
+$env:AZURE_AI_BING_CONNECTION_ID="/subscriptions/<sub-id>/resourceGroups/<rg>/providers/Microsoft.CognitiveServices/accounts/<account>/projects/<project>/connections/<connection-name>"

 # Optional, defaults to o3-deep-research
 $env:AZURE_AI_REASONING_DEPLOYMENT_NAME="o3-deep-research"
@@ -24,12 +24,12 @@ var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT
 Func<Task<string[]>> loadNextThreeCalendarEvents = async () =>
 {
    // In a real implementation, this method would connect to a calendar service
-    return new string[]
-    {
+    return
+    [
        "Doctor's appointment today at 15:00",
        "Team meeting today at 17:00",
        "Birthday party today at 20:00"
-    };
+    ];
 };

 // Create an agent with an AI context provider attached that aggregates two other providers:
@@ -87,7 +87,7 @@ namespace SampleApp
    internal sealed class TodoListAIContextProvider : AIContextProvider
    {
        private static List<string> GetTodoItems(AgentSession? session)
-            => session?.StateBag.GetValue<List<string>>(nameof(TodoListAIContextProvider)) ?? new List<string>();
+            => session?.StateBag.GetValue<List<string>>(nameof(TodoListAIContextProvider)) ?? [];

        private static void SetTodoItems(AgentSession? session, List<string> items)
            => session?.StateBag.SetValue(nameof(TodoListAIContextProvider), items);
@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,228 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how the ChatClientAgent persists chat history after each individual
+// call to the AI service, using the RequirePerServiceCallChatHistoryPersistence option.
+// When an agent uses tools, FunctionInvokingChatClient may loop multiple times
+// (service call → tool execution → service call), and intermediate messages (tool calls and
+// results) are persisted after each service call. This allows you to inspect or recover them
+// even if the process is interrupted mid-loop, but may also result in chat history that is not
+// yet finalized (e.g., tool calls without results) being persisted, which may be undesirable in some cases.
+//
+// To use end-of-run persistence instead (atomic run semantics), remove the
+// RequirePerServiceCallChatHistoryPersistence = true setting (or set it to false). End-of-run
+// persistence is the default behavior.
+//
+// The sample runs two multi-turn conversations: one using non-streaming (RunAsync) and one
+// using streaming (RunStreamingAsync), to demonstrate correct behavior in both modes.
+
+using System.ComponentModel;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using OpenAI.Responses;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+var store = Environment.GetEnvironmentVariable("AZURE_OPENAI_RESPONSES_STORE") ?? "false";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient openAIClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Define multiple tools so the model makes several tool calls in a single run.
+[Description("Get the current weather for a city.")]
+static string GetWeather([Description("The city name.")] string city) =>
+    city.ToUpperInvariant() switch
+    {
+        "SEATTLE" => "Seattle: 55°F, cloudy with light rain.",
+        "NEW YORK" => "New York: 72°F, sunny and warm.",
+        "LONDON" => "London: 48°F, overcast with fog.",
+        "DUBLIN" => "Dublin: 43°F, overcast with fog.",
+        _ => $"{city}: weather data not available."
+    };
+
+[Description("Get the current time in a city.")]
+static string GetTime([Description("The city name.")] string city) =>
+    city.ToUpperInvariant() switch
+    {
+        "SEATTLE" => "Seattle: 9:00 AM PST",
+        "NEW YORK" => "New York: 12:00 PM EST",
+        "LONDON" => "London: 5:00 PM GMT",
+        "DUBLIN" => "Dublin: 5:00 PM GMT",
+        _ => $"{city}: time data not available."
+    };
+
+// Create the agent — per-service-call persistence is enabled via RequirePerServiceCallChatHistoryPersistence.
+// The in-memory ChatHistoryProvider is used by default when the service does not require service stored chat
+// history, so for those cases, we can inspect the chat history via session.TryGetInMemoryChatHistory().
+IChatClient chatClient = string.Equals(store, "TRUE", StringComparison.OrdinalIgnoreCase) ?
+    openAIClient.GetResponsesClient().AsIChatClient(deploymentName) :
+    openAIClient.GetResponsesClient().AsIChatClientWithStoredOutputDisabled(deploymentName);
+AIAgent agent = chatClient.AsAIAgent(
+    new ChatClientAgentOptions
+    {
+        Name = "WeatherAssistant",
+        RequirePerServiceCallChatHistoryPersistence = true,
+        ChatOptions = new()
+        {
+            Instructions = "You are a helpful assistant. When asked about multiple cities, call the appropriate tool for each city.",
+            Tools = [AIFunctionFactory.Create(GetWeather), AIFunctionFactory.Create(GetTime)]
+        },
+    });
+
+await RunNonStreamingAsync();
+await RunStreamingAsync();
+
+async Task RunNonStreamingAsync()
+{
+    int lastChatHistorySize = 0;
+    string lastConversationId = string.Empty;
+
+    Console.ForegroundColor = ConsoleColor.Yellow;
+    Console.WriteLine("\n=== Non-Streaming Mode ===");
+    Console.ResetColor();
+
+    AgentSession session = await agent.CreateSessionAsync();
+
+    // First turn — ask about multiple cities so the model calls tools.
+    const string Prompt = "What's the weather and time in Seattle, New York, and London?";
+    PrintUserMessage(Prompt);
+
+    var response = await agent.RunAsync(Prompt, session);
+    PrintAgentResponse(response.Text);
+    PrintChatHistory(session, "After run", ref lastChatHistorySize, ref lastConversationId);
+
+    // Second turn — follow-up to verify chat history is correct.
+    const string FollowUp1 = "And Dublin?";
+    PrintUserMessage(FollowUp1);
+
+    response = await agent.RunAsync(FollowUp1, session);
+    PrintAgentResponse(response.Text);
+    PrintChatHistory(session, "After second run", ref lastChatHistorySize, ref lastConversationId);
+
+    // Third turn — follow-up to verify chat history is correct.
+    const string FollowUp2 = "Which city is the warmest?";
+    PrintUserMessage(FollowUp2);
+
+    response = await agent.RunAsync(FollowUp2, session);
+    PrintAgentResponse(response.Text);
+    PrintChatHistory(session, "After third run", ref lastChatHistorySize, ref lastConversationId);
+}
+
+async Task RunStreamingAsync()
+{
+    int lastChatHistorySize = 0;
+    string lastConversationId = string.Empty;
+
+    Console.ForegroundColor = ConsoleColor.Yellow;
+    Console.WriteLine("\n=== Streaming Mode ===");
+    Console.ResetColor();
+
+    AgentSession session = await agent.CreateSessionAsync();
+
+    // First turn — ask about multiple cities so the model calls tools.
+    const string Prompt = "What's the weather and time in Seattle, New York, and London?";
+    PrintUserMessage(Prompt);
+
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+
+    await foreach (var update in agent.RunStreamingAsync(Prompt, session))
+    {
+        Console.Write(update);
+
+        // During streaming we should be able to see updates to the chat history
+        // before the full run completes, as each service call is made and persisted.
+        PrintChatHistory(session, "During run", ref lastChatHistorySize, ref lastConversationId);
+    }
+
+    Console.WriteLine();
+    PrintChatHistory(session, "After run", ref lastChatHistorySize, ref lastConversationId);
+
+    // Second turn — follow-up to verify chat history is correct.
+    const string FollowUp1 = "And Dublin?";
+    PrintUserMessage(FollowUp1);
+
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+
+    await foreach (var update in agent.RunStreamingAsync(FollowUp1, session))
+    {
+        Console.Write(update);
+
+        // During streaming we should be able to see updates to the chat history
+        // before the full run completes, as each service call is made and persisted.
+        PrintChatHistory(session, "During second run", ref lastChatHistorySize, ref lastConversationId);
+    }
+
+    Console.WriteLine();
+    PrintChatHistory(session, "After second run", ref lastChatHistorySize, ref lastConversationId);
+
+    // Third turn — follow-up to verify chat history is correct.
+    const string FollowUp2 = "Which city is the warmest?";
+    PrintUserMessage(FollowUp2);
+
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+
+    await foreach (var update in agent.RunStreamingAsync(FollowUp2, session))
+    {
+        Console.Write(update);
+
+        // During streaming we should be able to see updates to the chat history
+        // before the full run completes, as each service call is made and persisted.
+        PrintChatHistory(session, "During third run", ref lastChatHistorySize, ref lastConversationId);
+    }
+
+    Console.WriteLine();
+    PrintChatHistory(session, "After third run", ref lastChatHistorySize, ref lastConversationId);
+}
+
+void PrintUserMessage(string message)
+{
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[User] ");
+    Console.ResetColor();
+    Console.WriteLine(message);
+}
+
+void PrintAgentResponse(string? text)
+{
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+    Console.WriteLine(text);
+}
+
+// Helper to print the current chat history from the session.
+void PrintChatHistory(AgentSession session, string label, ref int lastChatHistorySize, ref string lastConversationId)
+{
+    if (session.TryGetInMemoryChatHistory(out var history) && history.Count != lastChatHistorySize)
+    {
+        Console.ForegroundColor = ConsoleColor.DarkGray;
+        Console.WriteLine($"\n  [{label} — Chat history: {history.Count} message(s)]");
+        foreach (var msg in history)
+        {
+            var preview = msg.Text?.Length > 80 ? msg.Text[..80] + "…" : msg.Text;
+            var contentTypes = string.Join(", ", msg.Contents.Select(c => c.GetType().Name));
+            Console.WriteLine($"    {msg.Role,-12} | {(string.IsNullOrWhiteSpace(preview) ? $"[{contentTypes}]" : preview)}");
+        }
+
+        Console.ResetColor();
+
+        lastChatHistorySize = history.Count;
+    }
+
+    if (session is ChatClientAgentSession ccaSession && ccaSession.ConversationId is not null && ccaSession.ConversationId != lastConversationId)
+    {
+        Console.ForegroundColor = ConsoleColor.DarkGray;
+        Console.WriteLine($"  [{label} — Conversation ID: {ccaSession.ConversationId}]");
+        Console.ResetColor();
+        lastConversationId = ccaSession.ConversationId;
+    }
+}
@@ -0,0 +1,66 @@
+# In-Function-Loop Checkpointing
+
+This sample demonstrates how `ChatClientAgent` can persist chat history after each individual call to the AI service using the `RequirePerServiceCallChatHistoryPersistence` option. This per-service-call persistence ensures intermediate progress is saved during the function invocation loop.
+
+## What This Sample Shows
+
+When an agent uses tools, the `FunctionInvokingChatClient` loops multiple times (service call → tool execution → service call → …). By enabling `RequirePerServiceCallChatHistoryPersistence = true`, chat history is persisted after each service call via the `PerServiceCallChatHistoryPersistingChatClient` decorator:
+
+- A `PerServiceCallChatHistoryPersistingChatClient` decorator is inserted into the chat client pipeline
+- Before each service call, the decorator loads history from the `ChatHistoryProvider` and prepends it to the request
+- After each service call, the decorator notifies the `ChatHistoryProvider` (and any `AIContextProvider` instances) with the new messages
+- Only **new** messages are sent to providers on each notification — messages that were already persisted in an earlier call within the same run are deduplicated automatically
+
+By default (without `RequirePerServiceCallChatHistoryPersistence`), chat history is persisted at the end of the full agent run instead. To use per-service-call persistence, set `RequirePerServiceCallChatHistoryPersistence = true` on `ChatClientAgentOptions`.
+
+With `RequirePerServiceCallChatHistoryPersistence` = true, the behavior matches that of chat history stored in the underlying AI service exactly.
+
+Per-service-call persistence is useful for:
+- **Crash recovery** — if the process is interrupted mid-loop, the intermediate tool calls and results are already persisted
+- **Observability** — you can inspect the chat history while the agent is still running (e.g., during streaming)
+- **Long-running tool loops** — agents with many sequential tool calls benefit from incremental persistence
+
+## How It Works
+
+The sample asks the agent about the weather and time in three cities. The model calls the `GetWeather` and `GetTime` tools for each city, resulting in multiple service calls within a single `RunStreamingAsync` invocation. After the run completes, the sample prints the full chat history to show all the intermediate messages that were persisted along the way.
+
+### Pipeline Architecture
+
+```
+ChatClientAgent
+  └─ FunctionInvokingChatClient    (handles tool call loop)
+       └─ PerServiceCallChatHistoryPersistingChatClient  (persists after each service call)
+            └─ Leaf IChatClient            (Azure OpenAI)
+```
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Azure OpenAI service endpoint and model deployment
+- Azure CLI installed and authenticated
+
+**Note**: This sample uses `DefaultAzureCredential`. Sign in with `az login` before running. For production, prefer a specific credential such as `ManagedIdentityCredential`. For more information, see the [Azure CLI authentication documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+## Environment Variables
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"  # Required
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"                       # Optional, defaults to gpt-4o-mini
+```
+
+## Running the Sample
+
+```powershell
+cd dotnet/samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing
+dotnet run
+```
+
+## Expected Behavior
+
+The sample runs two conversation turns:
+
+1. **First turn** — asks about weather and time in three cities. The model calls `GetWeather` and `GetTime` tools (potentially in parallel or sequentially), then provides a summary. The chat history dump after the run shows all the intermediate tool call and result messages.
+
+2. **Second turn** — asks a follow-up question ("Which city is the warmest?") that uses the persisted conversation context. The chat history dump shows the full accumulated conversation.
+
+The chat history printout uses `session.TryGetInMemoryChatHistory()` to inspect the in-memory storage.
@@ -45,6 +45,7 @@ Before you begin, ensure you have the following prerequisites:
 |[Declarative agent](./Agent_Step16_Declarative/)|This sample demonstrates how to declaratively define an agent.|
 |[Providing additional AI Context to an agent using multiple AIContextProviders](./Agent_Step17_AdditionalAIContext/)|This sample demonstrates how to inject additional AI context into a ChatClientAgent using multiple custom AIContextProvider components that are attached to the agent.|
 |[Using compaction pipeline with an agent](./Agent_Step18_CompactionPipeline/)|This sample demonstrates how to use a compaction pipeline to efficiently limit the size of the conversation history for an agent.|
+|[In-function-loop checkpointing](./Agent_Step19_InFunctionLoopCheckpointing/)|This sample demonstrates how to persist chat history after each service call during a tool-calling loop, enabling crash recovery and mid-run observability.|

 ## Running the samples from the console

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">

  <PropertyGroup>
    <OutputType>Exe</OutputType>
@@ -0,0 +1,36 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create, use, and clean up a FoundryAgent backed by a server-side
+// versioned agent in Azure AI Foundry. It demonstrates the full lifecycle:
+// create agent version -> wrap as FoundryAgent -> run -> delete.
+
+using Azure.AI.Projects;
+using Azure.AI.Projects.Agents;
+using Azure.Identity;
+using Microsoft.Agents.AI.AzureAI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+const string JokerName = "JokerAgent";
+
+// Create the AIProjectClient to manage server-side agents.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
+
+// Create a server-side agent version using the native SDK.
+AgentVersion agentVersion = await aiProjectClient.Agents.CreateAgentVersionAsync(
+    JokerName,
+    new AgentVersionCreationOptions(
+        new PromptAgentDefinition(model: deploymentName)
+        {
+            Instructions = "You are good at telling jokes.",
+        }));
+
+// Wrap the agent version as a FoundryAgent using the AsAIAgent extension.
+FoundryAgent agent = aiProjectClient.AsAIAgent(agentVersion);
+
+// Once you have the agent, you can invoke it like any other AIAgent.
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
+
+// Cleanup: deletes the agent and all its versions.
+await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
@@ -0,0 +1,23 @@
+# Agent Step 00 - FoundryAgent Lifecycle
+
+This sample demonstrates the full lifecycle of a `FoundryAgent` backed by a server-side versioned agent in Microsoft Foundry: create → run → delete.
+
+## Prerequisites
+
+- A Microsoft Foundry project endpoint
+- A model deployment name (defaults to `gpt-4o-mini`)
+- Azure CLI installed and authenticated
+
+## Environment Variables
+
+| Variable | Description | Required |
+| --- | --- | --- |
+| `AZURE_AI_PROJECT_ENDPOINT` | Microsoft Foundry project endpoint | Yes |
+| `AZURE_AI_MODEL_DEPLOYMENT_NAME` | Model deployment name | No (defaults to `gpt-4o-mini`) |
+
+## Running the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step00_FoundryAgentLifecycle
+```
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,20 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create and run a basic agent with AIProjectClient.AsAIAgent(...).
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent =
+    new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
+    .AsAIAgent(model: deploymentName, instructions: "You are good at telling jokes.", name: "JokerAgent");
+
+// Once you have the agent, you can invoke it like any other AIAgent.
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
@@ -0,0 +1,55 @@
+# Creating and Running a Basic Agent with the Responses API
+
+This sample demonstrates how to create and run a basic AI agent using the `ChatClientAgent`, which uses the Microsoft Foundry Responses API directly without creating server-side agent definitions.
+
+## What this sample demonstrates
+
+- Creating a `ChatClientAgent` with instructions and a model
+- Running a simple single-turn conversation
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+Navigate to the AgentsWithFoundry sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step01_Basics
+```
+
+## Alternative: Composable approach
+
+You can also create the same agent by composing the underlying `IChatClient` directly. This gives you full control over the chat client pipeline:
+
+```csharp
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+AIAgent agent = new ChatClientAgent(
+    chatClient: aiProjectClient.GetProjectOpenAIClient().GetProjectResponsesClient().AsIChatClient(deploymentName),
+    instructions: "You are good at telling jokes.",
+    name: "JokerAgent");
+```
+
+This approach is useful when you need to customize the chat client pipeline or swap providers (e.g., Anthropic, OpenAI) while keeping the same agent code.
@@ -9,8 +9,7 @@
  </PropertyGroup>

  <ItemGroup>
-    <PackageReference Include="Azure.AI.Projects" />
-    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
  </ItemGroup>

 </Project>
@@ -0,0 +1,26 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create a multi-turn conversation agent using sessions.
+// Context is preserved across multiple runs via response ID chaining in the session.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
+    .AsAIAgent(deploymentName, instructions: "You are good at telling jokes.", name: "JokerAgent");
+
+// Create a session to maintain context across multiple runs.
+AgentSession session = await agent.CreateSessionAsync();
+
+// First turn
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", session));
+
+// Second turn — the agent remembers the first turn via the session.
+Console.WriteLine(await agent.RunAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", session));
@@ -0,0 +1,36 @@
+# Multi-turn Conversation
+
+This sample demonstrates how to implement multi-turn conversations where context is preserved across multiple agent runs using sessions and response ID chaining.
+
+## What this sample demonstrates
+
+- Creating an agent with instructions
+- Using sessions to maintain conversation context across multiple runs
+- Response ID chaining for multi-turn conversations
+- No server-side conversation creation required
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+Navigate to the AgentsWithFoundry sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step02.1_MultiturnConversation
+```
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,34 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use server-side conversations with a FoundryAgent.
+// Server-side conversations persist on the Foundry service and are visible in the Foundry Project UI.
+// Use this when you need conversation history to be stored and accessible server-side.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.AzureAI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+FoundryAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
+    .AsAIAgent(deploymentName, instructions: "You are good at telling jokes.", name: "JokerAgent");
+
+// CreateConversationSessionAsync creates a server-side ProjectConversation
+// that persists on the Foundry service and is visible in the Foundry Project UI.
+AgentSession session = await agent.CreateConversationSessionAsync();
+
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", session));
+Console.WriteLine(await agent.RunAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", session));
+
+// Streaming with server-side conversation context.
+await foreach (AgentResponseUpdate update in agent.RunStreamingAsync("Tell me another joke, but about a ninja this time.", session))
+{
+    Console.Write(update);
+}
+
+Console.WriteLine();
@@ -0,0 +1,36 @@
+# Multi-turn Conversation with Server-Side Conversations
+
+This sample demonstrates how to use server-side conversations with a `FoundryAgent`. Server-side conversations persist on the Foundry service and are visible in the Foundry Project UI, making them ideal when you need conversation history to be stored and accessible server-side.
+
+## What this sample demonstrates
+
+- Creating a `FoundryAgent` with instructions
+- Using `CreateConversationSessionAsync` to create a server-side `ProjectConversation`
+- Multi-turn conversations with both text and streaming output
+- Server-side conversation persistence visible in the Foundry Project UI
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+Navigate to the AgentsWithFoundry sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step02.2_MultiturnWithServerConversations
+```
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,41 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use function tools.
+
+using System.ComponentModel;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+[Description("Get the weather for a given location.")]
+static string GetWeather([Description("The location to get the weather for.")] string location)
+    => $"The weather in {location} is cloudy with a high of 15°C.";
+
+// Define the function tool.
+AITool tool = AIFunctionFactory.Create(GetWeather);
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Create a AIAgent with function tools.
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are a helpful assistant that can get weather information.",
+    name: "WeatherAssistant",
+    tools: [tool]);
+
+// Non-streaming agent interaction with function tools.
+AgentSession session = await agent.CreateSessionAsync();
+Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?", session));
+
+// Streaming agent interaction with function tools.
+session = await agent.CreateSessionAsync();
+await foreach (AgentResponseUpdate update in agent.RunStreamingAsync("What is the weather like in Amsterdam?", session))
+{
+    Console.Write(update);
+}
@@ -0,0 +1,37 @@
+# Using Function Tools with the Responses API
+
+This sample demonstrates how to use function tools with the `ChatClientAgent`, allowing the agent to call custom functions to retrieve information.
+
+## What this sample demonstrates
+
+- Creating function tools using `AIFunctionFactory`
+- Passing function tools to a `ChatClientAgent`
+- Running agents with function tools (text output)
+- Running agents with function tools (streaming output)
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+Navigate to the AgentsWithFoundry sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step03_UsingFunctionTools
+```
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -1,9 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.

 // This sample demonstrates how to use an agent with function tools that require a human in the loop for approvals.
-// It shows both non-streaming and streaming agent interactions using weather-related tools.
-// If the agent is hosted in a service, with a remote user, combine this sample with the Persisted Conversations sample to persist the chat history
-// while the agent is waiting for user input.

 using System.ComponentModel;
 using Azure.AI.Projects;
@@ -11,18 +8,13 @@ using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// Create a sample function tool that the agent can use.
 [Description("Get the weather for a given location.")]
 static string GetWeather([Description("The location to get the weather for.")] string location)
    => $"The weather in {location} is cloudy with a high of 15°C.";

-const string AssistantInstructions = "You are a helpful assistant that can get weather information.";
-const string AssistantName = "WeatherAssistant";
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";

-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
@@ -30,16 +22,16 @@ AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredent

 ApprovalRequiredAIFunction approvalTool = new(AIFunctionFactory.Create(GetWeather, name: nameof(GetWeather)));

-// Create AIAgent directly
-AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: AssistantName, model: deploymentName, instructions: AssistantInstructions, tools: [approvalTool]);
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are a helpful assistant that can get weather information.",
+    name: "WeatherAssistant",
+    tools: [approvalTool]);

 // Call the agent with approval-required function tools.
-// The agent will request approval before invoking the function.
 AgentSession session = await agent.CreateSessionAsync();
 AgentResponse response = await agent.RunAsync("What is the weather like in Amsterdam?", session);

 // Check if there are any approval requests.
-// For simplicity, we are assuming here that only function approvals are pending.
 List<ToolApprovalRequestContent> approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();

 while (approvalRequests.Count > 0)
@@ -53,13 +45,8 @@ while (approvalRequests.Count > 0)
            return new ChatMessage(ChatRole.User, [functionApprovalRequest.CreateResponse(approved)]);
        });

-    // Pass the user input responses back to the agent for further processing.
    response = await agent.RunAsync(userInputMessages, session);
-
    approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();
 }

 Console.WriteLine($"\nAgent: {response}");
-
-// Cleanup by agent name removes the agent version created.
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
@@ -0,0 +1,30 @@
+# Using Function Tools with Approvals via the Responses API
+
+This sample demonstrates how to use function tools that require human-in-the-loop approval before execution.
+
+## What this sample demonstrates
+
+- Creating function tools that require approval using `ApprovalRequiredAIFunction`
+- Handling approval requests from the agent
+- Passing approval responses back to the agent
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step04_UsingFunctionToolsWithApprovals
+```
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -15,29 +15,23 @@ using SampleApp;
 string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";

-const string AssistantInstructions = "You are a helpful assistant that extracts structured information about people.";
-const string AssistantName = "StructuredOutputAssistant";
-
-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

-// Create ChatClientAgent directly
-ChatClientAgent agent = await aiProjectClient.CreateAIAgentAsync(
-    model: deploymentName,
-    new ChatClientAgentOptions()
+AIAgent agent = aiProjectClient.AsAIAgent(new ChatClientAgentOptions
+{
+    Name = "StructuredOutputAssistant",
+    ChatOptions = new()
    {
-        Name = AssistantName,
-        ChatOptions = new()
-        {
-            Instructions = AssistantInstructions,
-            ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>()
-        }
-    });
+        ModelId = deploymentName,
+        Instructions = "You are a helpful assistant that extracts structured information about people.",
+        ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>()
+    }
+});

-// Set PersonInfo as the type parameter of RunAsync method to specify the expected structured output from the agent and invoke the agent with some unstructured input.
+// Set PersonInfo as the type parameter of RunAsync method to specify the expected structured output.
 AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("Please provide information about John Smith, who is a 35-year-old software engineer.");

 // Access the structured output via the Result property of the agent response.
@@ -46,39 +40,21 @@ Console.WriteLine($"Name: {response.Result.Name}");
 Console.WriteLine($"Age: {response.Result.Age}");
 Console.WriteLine($"Occupation: {response.Result.Occupation}");

-// Create the ChatClientAgent with the specified name, instructions, and expected structured output the agent should produce.
-ChatClientAgent agentWithPersonInfo = await aiProjectClient.CreateAIAgentAsync(
-    model: deploymentName,
-    new ChatClientAgentOptions()
-    {
-        Name = AssistantName,
-        ChatOptions = new()
-        {
-            Instructions = AssistantInstructions,
-            ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>()
-        }
-    });
+// Invoke the agent with streaming support, then deserialize the assembled response.
+IAsyncEnumerable<AgentResponseUpdate> updates = agent.RunStreamingAsync("Please provide information about Jane Doe, who is a 28-year-old data scientist.");

-// Invoke the agent with some unstructured input while streaming, to extract the structured information from.
-IAsyncEnumerable<AgentResponseUpdate> updates = agentWithPersonInfo.RunStreamingAsync("Please provide information about John Smith, who is a 35-year-old software engineer.");
-
-// Assemble all the parts of the streamed output, since we can only deserialize once we have the full json,
-// then deserialize the response into the PersonInfo class.
 PersonInfo personInfo = JsonSerializer.Deserialize<PersonInfo>((await updates.ToAgentResponseAsync()).Text, JsonSerializerOptions.Web)
    ?? throw new InvalidOperationException("Failed to deserialize the streamed response into PersonInfo.");

-Console.WriteLine("Assistant Output:");
+Console.WriteLine("\nStreaming Assistant Output:");
 Console.WriteLine($"Name: {personInfo.Name}");
 Console.WriteLine($"Age: {personInfo.Age}");
 Console.WriteLine($"Occupation: {personInfo.Occupation}");

-// Cleanup by agent name removes the agent version created.
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
-
 namespace SampleApp
 {
    /// <summary>
-    /// Represents information about a person, including their name, age, and occupation, matched to the JSON schema used in the agent.
+    /// Represents information about a person.
    /// </summary>
    [Description("Information about a person including their name, age, and occupation")]
    public class PersonInfo
@@ -0,0 +1,29 @@
+# Structured Output with the Responses API
+
+This sample demonstrates how to configure an agent to produce structured output using JSON schema.
+
+## What this sample demonstrates
+
+- Using `RunAsync<T>()` to get typed structured output from the agent
+- Deserializing streamed responses into structured types
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step05_StructuredOutput
+```
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -1,6 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to create and use a simple AI agent with a conversation that can be persisted to disk.
+// This sample shows how to persist and resume conversations.

 using System.Text.Json;
 using Azure.AI.Projects;
@@ -10,16 +10,14 @@ using Microsoft.Agents.AI;
 string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";

-const string JokerInstructions = "You are good at telling jokes.";
-const string JokerName = "JokerAgent";
-
-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

-AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: JokerName, model: deploymentName, instructions: JokerInstructions);
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are good at telling jokes.",
+    name: "JokerAgent");

 // Start a new session for the agent conversation.
 AgentSession session = await agent.CreateSessionAsync();
@@ -42,6 +40,3 @@ AgentSession resumedSession = await agent.DeserializeSessionAsync(reloadedSerial

 // Run the agent again with the resumed session.
 Console.WriteLine(await agent.RunAsync("Now tell the same joke in the voice of a pirate, and add some emojis to the joke.", resumedSession));
-
-// Cleanup by agent name removes the agent version created.
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
@@ -0,0 +1,30 @@
+# Persisted Conversations with the Responses API
+
+This sample demonstrates how to persist and resume agent conversations using session serialization.
+
+## What this sample demonstrates
+
+- Serializing agent sessions to JSON for persistence
+- Saving and loading sessions from disk
+- Resuming conversations with preserved context
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step06_PersistedConversations
+```
@@ -9,8 +9,6 @@
  </PropertyGroup>

  <ItemGroup>
-    <PackageReference Include="Azure.AI.Projects" />
-    <PackageReference Include="Azure.Identity" />
    <PackageReference Include="Azure.Monitor.OpenTelemetry.Exporter" />
    <PackageReference Include="OpenTelemetry" />
    <PackageReference Include="OpenTelemetry.Exporter.Console" />
@@ -1,6 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to create and use a simple AI agent with Azure Foundry Agents as the backend that logs telemetry using OpenTelemetry.
+// This sample shows how to add OpenTelemetry observability to an agent.

 using Azure.AI.Projects;
 using Azure.Identity;
@@ -9,15 +9,11 @@ using Microsoft.Agents.AI;
 using OpenTelemetry;
 using OpenTelemetry.Trace;

+string? applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");
 string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-string? applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");

-const string JokerInstructions = "You are good at telling jokes.";
-const string JokerName = "JokerAgent";
-
-// Create TracerProvider with console exporter
-// This will output the telemetry data to the console.
+// Create TracerProvider with console exporter.
 string sourceName = Guid.NewGuid().ToString("N");
 TracerProviderBuilder tracerProviderBuilder = Sdk.CreateTracerProviderBuilder()
    .AddSource(sourceName)
@@ -28,14 +24,16 @@ if (!string.IsNullOrWhiteSpace(applicationInsightsConnectionString))
 }
 using var tracerProvider = tracerProviderBuilder.Build();

-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

-// Define the agent you want to create. (Prompt Agent in this case)
-AIAgent agent = (await aiProjectClient.CreateAIAgentAsync(name: JokerName, model: deploymentName, instructions: JokerInstructions))
+AIAgent agent = aiProjectClient
+    .AsAIAgent(
+        deploymentName,
+        instructions: "You are good at telling jokes.",
+        name: "JokerAgent")
    .AsBuilder()
    .UseOpenTelemetry(sourceName: sourceName)
    .Build();
@@ -48,8 +46,7 @@ Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", session
 session = await agent.CreateSessionAsync();
 await foreach (AgentResponseUpdate update in agent.RunStreamingAsync("Tell me a joke about a pirate.", session))
 {
-    Console.WriteLine(update);
+    Console.Write(update);
 }

-// Cleanup by agent name removes the agent version created.
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
+Console.WriteLine();
@@ -0,0 +1,31 @@
+# Observability with the Responses API
+
+This sample demonstrates how to add OpenTelemetry observability to an agent using console and Azure Monitor exporters.
+
+## What this sample demonstrates
+
+- Configuring OpenTelemetry tracing with console exporter
+- Optional Azure Application Insights integration
+- Using `.AsBuilder().UseOpenTelemetry()` to add telemetry to the agent
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+$env:APPLICATIONINSIGHTS_CONNECTION_STRING="..."  # Optional
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step07_Observability
+```
@@ -11,8 +11,6 @@
  </PropertyGroup>

  <ItemGroup>
-    <PackageReference Include="Azure.AI.Projects" />
-    <PackageReference Include="Azure.Identity" />
    <PackageReference Include="Microsoft.Extensions.Hosting" />
  </ItemGroup>

@@ -0,0 +1,83 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use dependency injection to register a AIAgent and use it from a hosted service.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+using SampleApp;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are good at telling jokes.",
+    name: "JokerAgent");
+
+// Create a host builder that we will register services with and then run.
+HostApplicationBuilder builder = Host.CreateApplicationBuilder(args);
+
+// Add the AI agent to the service collection.
+builder.Services.AddSingleton(agent);
+
+// Add a sample service that will use the agent to respond to user input.
+builder.Services.AddHostedService<SampleService>();
+
+// Build and run the host.
+using IHost host = builder.Build();
+await host.RunAsync().ConfigureAwait(false);
+
+namespace SampleApp
+{
+    /// <summary>
+    /// A sample service that uses an AI agent to respond to user input.
+    /// </summary>
+    internal sealed class SampleService(AIAgent agent, IHostApplicationLifetime appLifetime) : IHostedService
+    {
+        private AgentSession? _session;
+
+        public async Task StartAsync(CancellationToken cancellationToken)
+        {
+            this._session = await agent.CreateSessionAsync(cancellationToken);
+            _ = this.RunAsync(appLifetime.ApplicationStopping);
+        }
+
+        public async Task RunAsync(CancellationToken cancellationToken)
+        {
+            await Task.Delay(100, cancellationToken);
+
+            while (!cancellationToken.IsCancellationRequested)
+            {
+                Console.WriteLine("\nAgent: Ask me to tell you a joke about a specific topic. To exit just press Ctrl+C or enter without any input.\n");
+                Console.Write("> ");
+                string? input = Console.ReadLine();
+
+                if (string.IsNullOrWhiteSpace(input))
+                {
+                    appLifetime.StopApplication();
+                    break;
+                }
+
+                await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(input, this._session, cancellationToken: cancellationToken))
+                {
+                    Console.Write(update);
+                }
+
+                Console.WriteLine();
+            }
+        }
+
+        public Task StopAsync(CancellationToken cancellationToken)
+        {
+            Console.WriteLine("\nShutting down...");
+            return Task.CompletedTask;
+        }
+    }
+}
@@ -0,0 +1,30 @@
+# Dependency Injection with the Responses API
+
+This sample demonstrates how to register a `ChatClientAgent` in a dependency injection container and use it from a hosted service.
+
+## What this sample demonstrates
+
+- Registering `ChatClientAgent` as an `AIAgent` in the service collection
+- Using the agent from a `IHostedService` with an interactive chat loop
+- Streaming responses in a hosted service context
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step08_DependencyInjection
+```
@@ -6,16 +6,15 @@

    <Nullable>enable</Nullable>
    <ImplicitUsings>enable</ImplicitUsings>
-    <NoWarn>$(NoWarn);CA1812</NoWarn>
  </PropertyGroup>

  <ItemGroup>
    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Azure.AI.Projects" />
    <PackageReference Include="ModelContextProtocol" />
  </ItemGroup>

  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
  </ItemGroup>

@@ -0,0 +1,44 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use MCP client tools with an agent.
+// It connects to the Microsoft Learn MCP server via HTTP and uses its tools.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using ModelContextProtocol.Client;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// Connect to the Microsoft Learn MCP server via HTTP (Streamable HTTP transport).
+Console.WriteLine("Connecting to MCP server at https://learn.microsoft.com/api/mcp ...");
+
+await using McpClient mcpClient = await McpClient.CreateAsync(new HttpClientTransport(new()
+{
+    Endpoint = new Uri("https://learn.microsoft.com/api/mcp"),
+    Name = "Microsoft Learn MCP",
+}));
+
+// Retrieve the list of tools available on the MCP server.
+IList<McpClientTool> mcpTools = await mcpClient.ListToolsAsync();
+Console.WriteLine($"MCP tools available: {string.Join(", ", mcpTools.Select(t => t.Name))}");
+
+List<AITool> agentTools = [.. mcpTools.Cast<AITool>()];
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are a helpful assistant that can help with Microsoft documentation questions. Use the Microsoft Learn MCP tool to search for documentation.",
+    name: "DocsAgent",
+    tools: agentTools);
+
+Console.WriteLine($"Agent '{agent.Name}' created. Asking a question...\n");
+
+const string Prompt = "How does one create an Azure storage account using az cli?";
+Console.WriteLine($"User: {Prompt}\n");
+Console.WriteLine($"Agent: {await agent.RunAsync(Prompt)}");
@@ -0,0 +1,29 @@
+# Using MCP Client as Tools with the Responses API
+
+This sample shows how to use MCP (Model Context Protocol) client tools with a `ChatClientAgent` using the Responses API directly.
+
+## What this sample demonstrates
+
+- Connecting to an MCP server via HTTP client transport
+- Retrieving MCP tools and passing them to a `ChatClientAgent`
+- Using MCP tools for agent interactions without server-side agent creation
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+- Node.js installed (for npx/MCP server)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+dotnet run
+```
@@ -0,0 +1,21 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <None Update="assets\walkway.jpg">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+  </ItemGroup>
+
+</Project>
@@ -1,6 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to use Image Multi-Modality with an AI agent.
+// This sample shows how to use image multi-modality with an agent.

 using Azure.AI.Projects;
 using Azure.Identity;
@@ -10,17 +10,14 @@ using Microsoft.Extensions.AI;
 string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o";

-const string VisionInstructions = "You are a helpful agent that can analyze images";
-const string VisionName = "VisionAgent";
-
-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

-// Define the agent you want to create. (Prompt Agent in this case)
-AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: VisionName, model: deploymentName, instructions: VisionInstructions);
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are a helpful agent that can analyze images.",
+    name: "VisionAgent");

 ChatMessage message = new(ChatRole.User, [
    new TextContent("What do you see in this image?"),
@@ -31,8 +28,7 @@ AgentSession session = await agent.CreateSessionAsync();

 await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(message, session))
 {
-    Console.WriteLine(update);
+    Console.Write(update);
 }

-// Cleanup by agent name removes the agent version created.
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
+Console.WriteLine();
@@ -0,0 +1,30 @@
+# Using Images with the Responses API
+
+This sample demonstrates how to use image multi-modality with an agent.
+
+## What this sample demonstrates
+
+- Loading images using `DataContent.LoadFromAsync`
+- Sending images alongside text to the agent
+- Streaming the agent's image analysis response
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and a vision-capable model deployment (e.g., `gpt-4o`)
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step10_UsingImages
+```
@@ -0,0 +1,15 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -1,6 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to create and use an Azure Foundry Agents AI agent as a function tool.
+// This sample shows how to use one agent as a function tool for another agent.

 using System.ComponentModel;
 using Azure.AI.Projects;
@@ -8,43 +8,29 @@ using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;

-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-const string WeatherInstructions = "You answer questions about the weather.";
-const string WeatherName = "WeatherAgent";
-const string MainInstructions = "You are a helpful assistant who responds in French.";
-const string MainName = "MainAgent";
-
 [Description("Get the weather for a given location.")]
 static string GetWeather([Description("The location to get the weather for.")] string location)
    => $"The weather in {location} is cloudy with a high of 15°C.";

-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
 // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
 // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
 // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
 AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());

-// Create the weather agent with function tools.
 AITool weatherTool = AIFunctionFactory.Create(GetWeather);
-AIAgent weatherAgent = await aiProjectClient.CreateAIAgentAsync(
-    name: WeatherName,
-    model: deploymentName,
-    instructions: WeatherInstructions,
+AIAgent weatherAgent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You answer questions about the weather.",
+    name: "WeatherAgent",
    tools: [weatherTool]);

-// Create the main agent, and provide the weather agent as a function tool.
-AIAgent agent = await aiProjectClient.CreateAIAgentAsync(
-    name: MainName,
-    model: deploymentName,
-    instructions: MainInstructions,
+AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are a helpful assistant who responds in French.",
+    name: "MainAgent",
    tools: [weatherAgent.AsAIFunction()]);

 // Invoke the agent and output the text result.
 AgentSession session = await agent.CreateSessionAsync();
 Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?", session));
-
-// Cleanup by agent name removes the agent versions created.
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
-await aiProjectClient.Agents.DeleteAgentAsync(weatherAgent.Name);
@@ -0,0 +1,30 @@
+# Agent as a Function Tool with the Responses API
+
+This sample demonstrates how to use one agent as a function tool for another agent.
+
+## What this sample demonstrates
+
+- Creating a specialized agent (weather) with function tools
+- Exposing an agent as a function tool using `.AsAIFunction()`
+- Composing agents where one agent delegates to another
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step11_AsFunctionTool
+```
@@ -3,15 +3,13 @@
  <PropertyGroup>
    <OutputType>Exe</OutputType>
    <TargetFrameworks>net10.0</TargetFrameworks>
-    
+
    <Nullable>enable</Nullable>
    <ImplicitUsings>enable</ImplicitUsings>
  </PropertyGroup>

  <ItemGroup>
    <PackageReference Include="Microsoft.Extensions.Logging.Console" />
-    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Azure.AI.Projects" />
  </ItemGroup>

  <ItemGroup>
@@ -1,6 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows multiple middleware layers working together with Azure Foundry Agents:
+// This sample shows multiple middleware layers working together with a ChatClientAgent:
 // agent run (PII filtering and guardrails),
 // function invocation (logging and result overrides), and human-in-the-loop
 // approval workflows for sensitive function calls.
@@ -12,19 +12,6 @@ using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;

-// Get Azure AI Foundry configuration from environment variables
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = System.Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o";
-
-const string AssistantInstructions = "You are an AI assistant that helps people find information.";
-const string AssistantName = "InformationAssistant";
-
-// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
-
 [Description("Get the weather for a given location.")]
 static string GetWeather([Description("The location to get the weather for.")] string location)
    => $"The weather in {location} is cloudy with a high of 15°C.";
@@ -33,14 +20,20 @@ static string GetWeather([Description("The location to get the weather for.")] s
 static string GetDateTime()
    => DateTimeOffset.Now.ToString();

+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
 AITool dateTimeTool = AIFunctionFactory.Create(GetDateTime, name: nameof(GetDateTime));
 AITool getWeatherTool = AIFunctionFactory.Create(GetWeather, name: nameof(GetWeather));

-// Define the agent you want to create. (Prompt Agent in this case)
-AIAgent originalAgent = await aiProjectClient.CreateAIAgentAsync(
-    name: AssistantName,
-    model: deploymentName,
-    instructions: AssistantInstructions,
+AIAgent originalAgent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are an AI assistant that helps people find information.",
+    name: "InformationAssistant",
    tools: [getWeatherTool, dateTimeTool]);

 // Adding middleware to the agent level
@@ -63,24 +56,17 @@ AgentResponse piiResponse = await middlewareEnabledAgent.RunAsync("My name is Jo
 Console.WriteLine($"Pii filtered response: {piiResponse}");

 Console.WriteLine("\n\n=== Example 3: Agent function middleware ===");
-
-// Agent function middleware support is limited to agents that wraps a upstream ChatClientAgent or derived from it.
-
 AgentResponse functionCallResponse = await middlewareEnabledAgent.RunAsync("What's the current time and the weather in Seattle?", session);
 Console.WriteLine($"Function calling response: {functionCallResponse}");

 // Special per-request middleware agent.
 Console.WriteLine("\n\n=== Example 4: Middleware with human in the loop function approval ===");

-AIAgent humanInTheLoopAgent = await aiProjectClient.CreateAIAgentAsync(
+AIAgent humanInTheLoopAgent = aiProjectClient.AsAIAgent(deploymentName,
+    instructions: "You are a Human in the loop testing AI assistant that helps people find information.",
    name: "HumanInTheLoopAgent",
-    model: deploymentName,
-    instructions: "You are an Human in the loop testing AI assistant that helps people find information.",
-
-    // Adding a function with approval required
    tools: [new ApprovalRequiredAIFunction(AIFunctionFactory.Create(GetWeather, name: nameof(GetWeather)))]);

-// Using the ConsolePromptingApprovalMiddleware for a specific request to handle user approval during function calls.
 AgentResponse response = await humanInTheLoopAgent
    .AsBuilder()
    .Use(ConsolePromptingApprovalMiddleware, null)
@@ -108,7 +94,6 @@ async ValueTask<object?> FunctionCallOverrideWeather(AIAgent agent, FunctionInvo

    if (context.Function.Name == nameof(GetWeather))
    {
-        // Override the result of the GetWeather function
        result = "The weather is sunny with a high of 25°C.";
    }
    Console.WriteLine($"Function Name: {context!.Function.Name} - Middleware 2 Post-Invoke");
@@ -118,18 +103,16 @@ async ValueTask<object?> FunctionCallOverrideWeather(AIAgent agent, FunctionInvo
 // This middleware redacts PII information from input and output messages.
 async Task<AgentResponse> PIIMiddleware(IEnumerable<ChatMessage> messages, AgentSession? session, AgentRunOptions? options, AIAgent innerAgent, CancellationToken cancellationToken)
 {
-    // Redact PII information from input messages
    var filteredMessages = FilterMessages(messages);
    Console.WriteLine("Pii Middleware - Filtered Messages Pre-Run");

-    var response = await innerAgent.RunAsync(filteredMessages, session, options, cancellationToken).ConfigureAwait(false);
+    var agentResponse = await innerAgent.RunAsync(filteredMessages, session, options, cancellationToken).ConfigureAwait(false);

-    // Redact PII information from output messages
-    response.Messages = FilterMessages(response.Messages);
+    agentResponse.Messages = FilterMessages(agentResponse.Messages);

    Console.WriteLine("Pii Middleware - Filtered Messages Post-Run");

-    return response;
+    return agentResponse;

    static IList<ChatMessage> FilterMessages(IEnumerable<ChatMessage> messages)
    {
@@ -138,11 +121,10 @@ async Task<AgentResponse> PIIMiddleware(IEnumerable<ChatMessage> messages, Agent

    static string FilterPii(string content)
    {
-        // Regex patterns for PII detection (simplified for demonstration)
        Regex[] piiPatterns = [
-            new(@"\b\d{3}-\d{3}-\d{4}\b", RegexOptions.Compiled), // Phone number (e.g., 123-456-7890)
-                    new(@"\b[\w\.-]+@[\w\.-]+\.\w+\b", RegexOptions.Compiled), // Email address
-                    new(@"\b[A-Z][a-z]+\s[A-Z][a-z]+\b", RegexOptions.Compiled) // Full name (e.g., John Doe)
+            MyRegex(),
+            EmailRegex(),
+            FullNameRegex()
        ];

        foreach (var pattern in piiPatterns)
@@ -157,20 +139,17 @@ async Task<AgentResponse> PIIMiddleware(IEnumerable<ChatMessage> messages, Agent
 // This middleware enforces guardrails by redacting certain keywords from input and output messages.
 async Task<AgentResponse> GuardrailMiddleware(IEnumerable<ChatMessage> messages, AgentSession? session, AgentRunOptions? options, AIAgent innerAgent, CancellationToken cancellationToken)
 {
-    // Redact keywords from input messages
    var filteredMessages = FilterMessages(messages);

    Console.WriteLine("Guardrail Middleware - Filtered messages Pre-Run");

-    // Proceed with the agent run
-    var response = await innerAgent.RunAsync(filteredMessages, session, options, cancellationToken);
+    var agentResponse = await innerAgent.RunAsync(filteredMessages, session, options, cancellationToken);

-    // Redact keywords from output messages
-    response.Messages = FilterMessages(response.Messages);
+    agentResponse.Messages = FilterMessages(agentResponse.Messages);

    Console.WriteLine("Guardrail Middleware - Filtered messages Post-Run");

-    return response;
+    return agentResponse;

    List<ChatMessage> FilterMessages(IEnumerable<ChatMessage> messages)
    {
@@ -194,16 +173,13 @@ async Task<AgentResponse> GuardrailMiddleware(IEnumerable<ChatMessage> messages,
 // This middleware handles Human in the loop console interaction for any user approval required during function calling.
 async Task<AgentResponse> ConsolePromptingApprovalMiddleware(IEnumerable<ChatMessage> messages, AgentSession? session, AgentRunOptions? options, AIAgent innerAgent, CancellationToken cancellationToken)
 {
-    AgentResponse response = await innerAgent.RunAsync(messages, session, options, cancellationToken);
+    AgentResponse agentResponse = await innerAgent.RunAsync(messages, session, options, cancellationToken);

-    // For simplicity, we are assuming here that only function approvals are pending.
-    List<ToolApprovalRequestContent> approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();
+    List<ToolApprovalRequestContent> approvalRequests = agentResponse.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();

    while (approvalRequests.Count > 0)
    {
-        // Ask the user to approve each function call request.
-        // Pass the user input responses back to the agent for further processing.
-        response.Messages = approvalRequests
+        agentResponse.Messages = approvalRequests
            .ConvertAll(functionApprovalRequest =>
            {
                Console.WriteLine($"The agent would like to invoke the following function, please reply Y to approve: Name {((FunctionCallContent)functionApprovalRequest.ToolCall).Name}");
@@ -211,13 +187,22 @@ async Task<AgentResponse> ConsolePromptingApprovalMiddleware(IEnumerable<ChatMes
                return new ChatMessage(ChatRole.User, [functionApprovalRequest.CreateResponse(approved)]);
            });

-        response = await innerAgent.RunAsync(response.Messages, session, options, cancellationToken);
+        agentResponse = await innerAgent.RunAsync(agentResponse.Messages, session, options, cancellationToken);

-        approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();
+        approvalRequests = agentResponse.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();
    }

-    return response;
+    return agentResponse;
 }

-// Cleanup by agent name removes the agent version created.
-await aiProjectClient.Agents.DeleteAgentAsync(middlewareEnabledAgent.Name);
+internal partial class Program
+{
+    [GeneratedRegex(@"\b\d{3}-\d{3}-\d{4}\b", RegexOptions.Compiled)]
+    private static partial Regex MyRegex();
+
+    [GeneratedRegex(@"\b[\w\.-]+@[\w\.-]+\.\w+\b", RegexOptions.Compiled)]
+    private static partial Regex EmailRegex();
+
+    [GeneratedRegex(@"\b[A-Z][a-z]+\s[A-Z][a-z]+\b", RegexOptions.Compiled)]
+    private static partial Regex FullNameRegex();
+}
@@ -0,0 +1,31 @@
+# Middleware with the Responses API
+
+This sample demonstrates multiple middleware layers working together: PII filtering, guardrails, function invocation logging, and human-in-the-loop approval.
+
+## What this sample demonstrates
+
+- Agent-level run middleware (PII filtering, guardrail enforcement)
+- Function-level middleware (logging, result overrides)
+- Human-in-the-loop approval workflows for sensitive function calls
+- Using `.AsBuilder().Use()` to compose middleware
+- No server-side agent creation or cleanup required
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Microsoft Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
+```
+
+## Run the sample
+
+```powershell
+cd dotnet/samples/02-agents/AgentsWithFoundry
+dotnet run --project .\Agent_Step12_Middleware
+```
--- a/Show More
+++ b/Show More