Merge branch 'main' into taochen/python-update-sample-validation-scripts

.github/actions/sample-validation-setup/action.yml

@@ -34,7 +34,7 @@ runs:
 
     - name: Test Copilot CLI
       shell: bash
-      run: copilot --version && copilot -p "What can you do in one sentence?"
+      run: copilot -p "What can you do in one sentence?"
 
     - name: Azure CLI Login
       uses: azure/login@v2

.github/actions/setup-local-mcp-server/action.yml

@@ -1,166 +0,0 @@
-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"

.github/workflows/python-check-coverage.py

@@ -41,7 +41,8 @@ ENFORCED_TARGETS: set[str] = {
     "packages.purview.agent_framework_purview",
     "packages.anthropic.agent_framework_anthropic",
     "packages.azure-ai-search.agent_framework_azure_ai_search",
-    "packages.openai.agent_framework_openai",
+    "packages.core.agent_framework.azure",
+    "packages.core.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

.github/workflows/python-integration-tests.yml

@@ -60,10 +60,9 @@ jobs:
     environment: integration
     timeout-minutes: 60
     env:
-      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_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
+      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
+      OPENAI_EMBEDDINGS_MODEL_ID: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
       OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
     defaults:
       run:
@@ -82,8 +81,8 @@ jobs:
       - name: Test with pytest (OpenAI integration)
         run: >
           uv run pytest --import-mode=importlib
-          packages/openai/tests
-          -m "integration and not azure"
+          packages/core/tests/openai
+          -m integration
           -n logical --dist worksteal
           --timeout=120 --session-timeout=900 --timeout_method thread
           --retries 2 --retry-delay 5
@@ -97,8 +96,7 @@ jobs:
     env:
       AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
       AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME }}
+      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
       AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
     defaults:
       run:
@@ -123,11 +121,7 @@ jobs:
       - name: Test with pytest (Azure OpenAI integration)
         run: >
           uv run pytest --import-mode=importlib
-          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
-          packages/azure-ai/tests/azure_openai
-          --ignore=packages/azure-ai/tests/azure_openai/test_azure_responses_client_foundry.py
+          packages/core/tests/azure
           -m integration
           -n logical --dist worksteal
           --timeout=120 --session-timeout=900 --timeout_method thread
@@ -157,13 +151,6 @@ 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 }}" >> "$GITHUB_ENV"
       - name: Test with pytest (Anthropic, Ollama, MCP integration)
         run: >
           uv run pytest --import-mode=importlib
@@ -174,26 +161,6 @@ jobs:
           -n logical --dist worksteal
           --timeout=120 --session-timeout=900 --timeout_method thread
           --retries 2 --retry-delay 5
-      - name: Stop local MCP server
-        if: always()
-        shell: bash
-        run: |
-          set -euo pipefail
-          server_pid="${{ 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:
@@ -203,16 +170,12 @@ jobs:
     timeout-minutes: 60
     env:
       UV_PYTHON: "3.11"
-      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_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
+      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
       OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
-      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 }}
+      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
       FUNCTIONS_WORKER_RUNTIME: "python"
       DURABLE_TASK_SCHEDULER_CONNECTION_STRING: "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None"
       AzureWebJobsStorage: "UseDevelopmentStorage=true"
@@ -246,23 +209,18 @@ jobs:
           packages/durabletask/tests/integration_tests
           -m integration
           -n logical --dist worksteal
-          -x
-          --timeout=360 --session-timeout=900 --timeout_method thread
+          --timeout=120 --session-timeout=900 --timeout_method thread
           --retries 2 --retry-delay 5
 
-  # Foundry integration tests
-  python-tests-foundry:
-    name: Python Integration Tests - Foundry
+  # Azure AI integration tests
+  python-tests-azure-ai:
+    name: Python Integration Tests - Azure AI
     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:
@@ -286,14 +244,7 @@ jobs:
           subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
       - name: Test with pytest
         timeout-minutes: 15
-        run: >
-          uv run pytest --import-mode=importlib
-          packages/azure-ai/tests/azure_openai/test_azure_responses_client_foundry.py
-          packages/foundry/tests
-          -m integration
-          -n logical --dist worksteal
-          --timeout=120 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
+        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
 
   # Azure Cosmos integration tests
   python-tests-cosmos:
@@ -350,7 +301,7 @@ jobs:
         python-tests-azure-openai,
         python-tests-misc-integration,
         python-tests-functions,
-        python-tests-foundry,
+        python-tests-azure-ai,
         python-tests-cosmos
       ]
     steps:

.github/workflows/python-merge-tests.yml

@@ -47,9 +47,6 @@ 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/**'
@@ -57,30 +54,20 @@ jobs:
               - 'python/packages/core/agent_framework/observability.py'
             openai:
               - 'python/packages/core/agent_framework/openai/**'
-              - 'python/packages/openai/**'
-              - 'python/samples/**/providers/openai/**'
+              - 'python/packages/core/tests/openai/**'
             azure:
-              - 'python/packages/openai/**'
               - 'python/packages/core/agent_framework/azure/**'
-              - 'python/packages/azure-ai/agent_framework_azure_ai/_deprecated_azure_openai.py'
-              - 'python/packages/azure-ai/tests/azure_openai/**'
-              - 'python/samples/**/providers/azure/openai_chat_completion_client_azure*.py'
+              - 'python/packages/core/tests/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
@@ -141,10 +128,9 @@ jobs:
     runs-on: ubuntu-latest
     environment: integration
     env:
-      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_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
+      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
+      OPENAI_EMBEDDINGS_MODEL_ID: ${{ vars.OPENAI_EMBEDDING_MODEL_ID }}
       OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
     defaults:
       run:
@@ -160,8 +146,8 @@ jobs:
       - name: Test with pytest (OpenAI integration)
         run: >
           uv run pytest --import-mode=importlib
-          packages/openai/tests
-          -m "integration and not azure"
+          packages/core/tests/openai
+          -m integration
           -n logical --dist worksteal
           --timeout=120 --session-timeout=900 --timeout_method thread
           --retries 2 --retry-delay 5
@@ -196,8 +182,7 @@ jobs:
     env:
       AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
       AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME }}
+      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
       AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
     defaults:
       run:
@@ -220,11 +205,7 @@ jobs:
       - name: Test with pytest (Azure OpenAI integration)
         run: >
           uv run pytest --import-mode=importlib
-          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
-          packages/azure-ai/tests/azure_openai
-          --ignore=packages/azure-ai/tests/azure_openai/test_azure_responses_client_foundry.py
+          packages/core/tests/azure
           -m integration
           -n logical --dist worksteal
           --timeout=120 --session-timeout=900 --timeout_method thread
@@ -272,13 +253,6 @@ 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 }}" >> "$GITHUB_ENV"
       - name: Test with pytest (Anthropic, Ollama, MCP integration)
         run: >
           uv run pytest --import-mode=importlib
@@ -290,26 +264,6 @@ jobs:
           --timeout=120 --session-timeout=900 --timeout_method thread
           --retries 2 --retry-delay 5
         working-directory: ./python
-      - name: Stop local MCP server
-        if: always()
-        shell: bash
-        run: |
-          set -euo pipefail
-          server_pid="${{ 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
@@ -334,16 +288,12 @@ jobs:
     environment: integration
     env:
       UV_PYTHON: "3.11"
-      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_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
+      OPENAI_RESPONSES_MODEL_ID: ${{ vars.OPENAI__RESPONSESMODELID }}
       OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
-      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 }}
+      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
       FUNCTIONS_WORKER_RUNTIME: "python"
       DURABLE_TASK_SCHEDULER_CONNECTION_STRING: "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None"
       AzureWebJobsStorage: "UseDevelopmentStorage=true"
@@ -375,8 +325,7 @@ jobs:
           packages/durabletask/tests/integration_tests
           -m integration
           -n logical --dist worksteal
-          -x
-          --timeout=360 --session-timeout=900 --timeout_method thread
+          --timeout=120 --session-timeout=900 --timeout_method thread
           --retries 2 --retry-delay 5
         working-directory: ./python
       - name: Surface failing tests
@@ -389,8 +338,8 @@ jobs:
           fail-on-empty: false
           title: Functions integration test results
 
-  python-tests-foundry:
-    name: Python Integration Tests - Foundry
+  python-tests-azure-ai:
+    name: Python Tests - Azure AI
     needs: paths-filter
     if: >
       github.event_name != 'pull_request' &&
@@ -403,10 +352,6 @@ 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:
@@ -428,14 +373,7 @@ jobs:
           subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
       - name: Test with pytest
         timeout-minutes: 15
-        run: >
-          uv run pytest --import-mode=importlib
-          packages/azure-ai/tests/azure_openai/test_azure_responses_client_foundry.py
-          packages/foundry/tests
-          -m integration
-          -n logical --dist worksteal
-          --timeout=120 --session-timeout=900 --timeout_method thread
-          --retries 2 --retry-delay 5
+        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
         working-directory: ./python
       - name: Test Azure AI samples
         timeout-minutes: 10
@@ -522,7 +460,7 @@ jobs:
         python-tests-azure-openai,
         python-tests-misc-integration,
         python-tests-functions,
-        python-tests-foundry,
+        python-tests-azure-ai,
         python-tests-cosmos,
       ]
     steps:

.github/workflows/python-sample-validation.yml

@@ -67,20 +67,17 @@ jobs:
       # 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 }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
       # 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_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME }}
+      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:
@@ -99,8 +96,6 @@ jobs:
 
       - name: Create .env for samples
         run: |
-          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
-          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
           echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
           echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
           echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
@@ -129,7 +124,6 @@ jobs:
     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: ${{ vars.OPENAI__RESPONSESMODELID }}
     defaults:
@@ -149,7 +143,6 @@ jobs:
       - name: Create .env for samples
         run: |
           echo "OPENAI_API_KEY=$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
 
@@ -164,8 +157,8 @@ jobs:
           name: validation-report-02-agents-openai
           path: python/samples/sample_validation/reports/
 
-  validate-02-agents-azure:
-    name: Validate 02-agents/providers/azure
+  validate-02-agents-azure-openai:
+    name: Validate 02-agents/providers/azure_openai
     runs-on: ubuntu-latest
     environment: integration
     env:
@@ -196,13 +189,93 @@ jobs:
 
       - 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
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure_openai --save-report --report-name 02-agents-azure-openai
 
       - name: Upload validation report
         uses: actions/upload-artifact@v7
         if: always()
         with:
-          name: validation-report-02-agents-azure
+          name: validation-report-02-agents-azure-openai
+          path: python/samples/sample_validation/reports/
+
+  validate-02-agents-azure-ai:
+    name: Validate 02-agents/providers/azure_ai
+    runs-on: ubuntu-latest
+    environment: integration
+    env:
+      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
+      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
+      AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
+      BING_CONNECTION_ID: ${{ secrets.BING_CONNECTION_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: ${{ runner.os }}
+
+      - name: Create .env for samples
+        run: |
+          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
+          echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME=$AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME=$AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME" >> .env
+          echo "BING_CONNECTION_ID=$BING_CONNECTION_ID" >> .env
+
+      - name: Run sample validation
+        run: |
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure_ai --save-report --report-name 02-agents-azure-ai
+
+      - name: Upload validation report
+        uses: actions/upload-artifact@v7
+        if: always()
+        with:
+          name: validation-report-02-agents-azure-ai
+          path: python/samples/sample_validation/reports/
+
+  validate-02-agents-azure-ai-agent:
+    name: Validate 02-agents/providers/azure_ai_agent
+    runs-on: ubuntu-latest
+    environment: integration
+    env:
+      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
+      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ 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: ${{ runner.os }}
+
+      - name: Create .env for samples
+        run: |
+          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
+          echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
+
+      - name: Run sample validation
+        run: |
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure_ai_agent --save-report --report-name 02-agents-azure-ai-agent
+
+      - name: Upload validation report
+        uses: actions/upload-artifact@v7
+        if: always()
+        with:
+          name: validation-report-02-agents-azure-ai-agent
           path: python/samples/sample_validation/reports/
 
   validate-02-agents-anthropic:
@@ -273,7 +346,7 @@ jobs:
 
   validate-02-agents-amazon:
     name: Validate 02-agents/providers/amazon
-    if: false # Temporarily disabled - requires AWS credentials
+    if: false  # Temporarily disabled - requires AWS credentials
     runs-on: ubuntu-latest
     environment: integration
     env:
@@ -305,7 +378,7 @@ jobs:
 
   validate-02-agents-ollama:
     name: Validate 02-agents/providers/ollama
-    if: false # Temporarily disabled - requires local Ollama server
+    if: false  # Temporarily disabled - requires local Ollama server
     runs-on: ubuntu-latest
     environment: integration
     env:
@@ -335,13 +408,11 @@ jobs:
           name: validation-report-02-agents-ollama
           path: python/samples/sample_validation/reports/
 
-  validate-02-agents-foundry:
-    name: Validate 02-agents/providers/foundry
+  validate-02-agents-foundry-local:
+    name: Validate 02-agents/providers/foundry_local
+    if: false  # Temporarily disabled - requires local Foundry setup
     runs-on: ubuntu-latest
     environment: integration
-    env:
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
     defaults:
       run:
         working-directory: python
@@ -356,25 +427,20 @@ 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
-
       - 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
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/foundry_local --save-report --report-name 02-agents-foundry-local
 
       - name: Upload validation report
         uses: actions/upload-artifact@v7
         if: always()
         with:
-          name: validation-report-02-agents-foundry
+          name: validation-report-02-agents-foundry-local
           path: python/samples/sample_validation/reports/
 
   validate-02-agents-copilotstudio:
     name: Validate 02-agents/providers/copilotstudio
-    if: false # Temporarily disabled - requires Copilot Studio setup
+    if: false  # Temporarily disabled - requires Copilot Studio setup
     runs-on: ubuntu-latest
     environment: integration
     env:
@@ -449,8 +515,6 @@ jobs:
     environment: integration
     env:
       # Azure AI configuration
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
       AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
       AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
       # Azure OpenAI configuration
@@ -473,8 +537,6 @@ jobs:
 
       - name: Create .env for samples
         run: |
-          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
-          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
           echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
           echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
           echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
@@ -494,7 +556,7 @@ jobs:
 
   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:
@@ -533,7 +595,7 @@ jobs:
 
   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:
@@ -590,7 +652,6 @@ jobs:
       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
@@ -642,7 +703,6 @@ jobs:
       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 }}
@@ -696,12 +756,14 @@ jobs:
       - validate-01-get-started
       - validate-02-agents
       - validate-02-agents-openai
-      - validate-02-agents-azure
+      - validate-02-agents-azure-openai
+      - validate-02-agents-azure-ai
+      - validate-02-agents-azure-ai-agent
       - validate-02-agents-anthropic
       - validate-02-agents-github-copilot
       - validate-02-agents-amazon
       - validate-02-agents-ollama
-      - validate-02-agents-foundry
+      - validate-02-agents-foundry-local
       - validate-02-agents-copilotstudio
       - validate-02-agents-custom
       - validate-03-workflows

docs/decisions/0020-foundry-agent-type-naming.md

@@ -1,125 +0,0 @@
----
-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.

docs/decisions/0021-agent-skills-design.md

@@ -1,960 +0,0 @@
-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.

docs/decisions/0021-provider-leading-clients.md

@@ -1,72 +0,0 @@
----
-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`.

docs/decisions/0022-chat-history-persistence-consistency.md

@@ -1,116 +0,0 @@
----
-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.
-
-This means the trimming feature (introduced in [PR #4792](https://github.com/microsoft/agent-framework/pull/4792)) is primarily needed as a complement to per-run persistence. The `PersistChatHistoryAtEndOfRun` setting (introduced in [PR #4762](https://github.com/microsoft/agent-framework/pull/4762)) inverts the default so that per-service-call persistence is the standard behavior, and per-run persistence is opt-in.
-
-## 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: Default to per-run persistence with `FunctionResultContent` trimming (opt-in to per-service-call)
-- Option 2: Default to per-service-call persistence (opt-in to per-run)
-
-## Pros and Cons of the Options
-
-### Option 1: Default to per-run persistence with `FunctionResultContent` trimming
-
-Keep the current default behavior of persisting chat history only at the end of the full agent run. Add `FunctionResultContent` trimming as the default to improve consistency with service storage. Provide an opt-in setting for users who want per-service-call persistence.
-
-Settings:
-- `PersistChatHistoryAtEndOfRun` = `true`
-
-- 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.
-- Good, because users can opt in to per-service-call persistence for checkpointing/recovery scenarios, satisfying drivers C and E.
-- 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 by default.
-
-### Option 2: Default to per-service-call persistence
-
-Change the default 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). Provide an opt-in setting for users who want per-run atomicity with trimming.
-
-Settings:
-- `PersistChatHistoryAtEndOfRun` = `false` (default)
-
-- Good, because the stored history matches the service's behavior by default 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 — Default to per-service-call persistence**, because it fully satisfies the consistency driver (A), naturally handles `FunctionResultContent` trimming without additional logic, and provides better recoverability for long-running tool-calling loops. Per-run persistence remains available via the `PersistChatHistoryAtEndOfRun` setting for users who prefer atomic run semantics.
-
-### Configuration Matrix
-
-The behavior depends on the combination of `UseProvidedChatClientAsIs` and `PersistChatHistoryAtEndOfRun`:
-
-| `UseProvidedChatClientAsIs` | `PersistChatHistoryAtEndOfRun` | Behavior |
-|---|---|---|
-| `false` (default) | `false` (default) | **Per-service-call persistence.** A `ChatHistoryPersistingChatClient` middleware is automatically injected into the chat client pipeline between `FunctionInvokingChatClient` and the leaf `IChatClient`. Messages are persisted after each service call. |
-| `true` | `false` | **User responsibility.** No middleware is injected because the user has provided a custom chat client stack. The user is responsible for ensuring correct persistence behavior (e.g., by including their own persisting middleware). |
-| `false` | `true` | **Per-run persistence with marking.** A `ChatHistoryPersistingChatClient` middleware is injected, but configured to *mark* messages with metadata rather than store them immediately. At the end of the run, marked messages are stored. Trailing `FunctionResultContent` is trimmed. |
-| `true` | `true` | **Per-run persistence with warning.** The system checks whether the custom chat client stack includes a `ChatHistoryPersistingChatClient`. If not, a warning is emitted (particularly relevant for workflow handoff scenarios where trimming cannot be guaranteed). If no `ChatHistoryPersistingChatClient` is preset, all messages are stored at the end of the run, otherwise marked messages are stored. |
-
-### Consequences
-
-- Good, because the stored history matches the service's behavior by default for both timing and content, fully satisfying consistency (driver A).
-- Good, because intermediate progress is preserved if the process is interrupted, satisfying recoverability (driver C).
-- Good, because no separate `FunctionResultContent` trimming logic is needed in the default path, reducing complexity.
-- Good, because marking persisted messages with metadata enables deduplication and aids debugging.
-- Good, because warnings for custom chat client configurations without the persisting middleware help prevent silent failures in workflow handoff scenarios.
-- Bad, because chat history may be left in an incomplete state if the run fails mid-loop (e.g., `FunctionCallContent` stored without corresponding `FunctionResultContent`), requiring manual recovery in rare cases.
-- Bad, because the mental model is more complex for the default path: a single run may produce multiple history updates.
-- Neutral, because users who prefer atomic run semantics can opt in to per-run persistence via `PersistChatHistoryAtEndOfRun = true`.
-- 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
-
-The `ChatHistoryPersistingChatClient` middleware must also update the session's `ConversationId` consistently for both response-based and conversation-based service interactions, ensuring the session always reflects the latest service-provided identifier.
-
-## More Information
-
-- [PR #4762: Persist messages during function call loop](https://github.com/microsoft/agent-framework/pull/4762) — introduces `PersistChatHistoryAfterEachServiceCall` option and `ChatHistoryPersistingChatClient` decorator
-- [PR #4792: Trim final FRC to match service storage](https://github.com/microsoft/agent-framework/pull/4792) — introduces `StoreFinalFunctionResultContent` option and `FilterFinalFunctionResultContent` logic
-- [Issue #2889](https://github.com/microsoft/agent-framework/issues/2889) — original issue tracking chat history persistence during function call loops

dotnet/agent-framework-dotnet.slnx

@@ -1,4 +1,4 @@
-<Solution>
+<Solution>
   <Configurations>
     <BuildType Name="Debug" />
     <BuildType Name="Publish" />
@@ -57,7 +57,6 @@
     <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" />
@@ -77,8 +76,6 @@
     <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" />
@@ -104,7 +101,7 @@
   </Folder>
   <Folder Name="/Samples/02-agents/AgentSkills/">
     <File Path="samples/02-agents/AgentSkills/README.md" />
-    <Project Path="samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/Agent_Step01_FileBasedSkills.csproj" />
+    <Project Path="samples/02-agents/AgentSkills/Agent_Step01_BasicSkills/Agent_Step01_BasicSkills.csproj" />
   </Folder>
   <Folder Name="/Samples/02-agents/AGUI/Step05_StateManagement/">
     <Project Path="samples/02-agents/AGUI/Step05_StateManagement/Client/Client.csproj" />
@@ -121,34 +118,6 @@
     <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" />
@@ -171,6 +140,35 @@
     <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" />
@@ -317,8 +315,8 @@
   <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/RazorWebClient/RazorWebClient.csproj" />
     <Project Path="samples/05-end-to-end/AspNetAgentAuthorization/Service/Service.csproj" />
+    <Project Path="samples/05-end-to-end/AspNetAgentAuthorization/RazorWebClient/RazorWebClient.csproj" />
   </Folder>
   <Folder Name="/Solution Items/">
     <File Path=".editorconfig" />

dotnet/samples/02-agents/AGUI/Step04_HumanInLoop/Client/Program.cs

@@ -70,7 +70,7 @@ while ((input = Console.ReadLine()) != null && !input.Equals("exit", StringCompa
 
                         if (approvalRequest.AdditionalProperties != null)
                         {
-                            approvalResponse.AdditionalProperties = [];
+                            approvalResponse.AdditionalProperties = new AdditionalPropertiesDictionary();
                             foreach (var kvp in approvalRequest.AdditionalProperties)
                             {
                                 approvalResponse.AdditionalProperties[kvp.Key] = kvp.Value;

dotnet/samples/02-agents/AGUI/Step04_HumanInLoop/Client/ServerFunctionApprovalClientAgent.cs

@@ -131,9 +131,9 @@ internal sealed class ServerFunctionApprovalClientAgent : DelegatingAIAgent
                     transformedContents ??= CopyContentsUpToIndex(message.Contents, contentIndex);
                     approvalCalls.Remove(functionResult.CallId);
                 }
-                else
+                else if (transformedContents != null)
                 {
-                    transformedContents?.Add(content);
+                    transformedContents.Add(content);
                 }
             }
 
@@ -155,10 +155,10 @@ internal sealed class ServerFunctionApprovalClientAgent : DelegatingAIAgent
                 result ??= CopyMessagesUpToIndex(messages, messageIndex);
                 result.Add(newMessage);
             }
-            else
+            else if (result != null)
             {
                 // 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
         }

dotnet/samples/02-agents/AGUI/Step04_HumanInLoop/Server/ServerFunctionApprovalServerAgent.cs

@@ -57,10 +57,16 @@ 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) ?? throw new InvalidOperationException("Failed to deserialize approval request from tool call");
+            approvalRequest != null ? approvalRequest : null;
+
+        if (request == null)
+        {
+            throw new InvalidOperationException("Failed to deserialize approval request from tool call");
+        }
+
         return new ToolApprovalRequestContent(
             requestId: request.ApprovalId,
             new FunctionCallContent(
@@ -71,11 +77,17 @@ 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) ?? throw new InvalidOperationException("Failed to deserialize approval response from tool result");
+                result.Result as ApprovalResponse;
+
+        if (approvalResponse == null)
+        {
+            throw new InvalidOperationException("Failed to deserialize approval response from tool result");
+        }
+
         return approval.CreateResponse(approvalResponse.Approved);
     }
 #pragma warning restore MEAI001
@@ -109,7 +121,7 @@ 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 = []; // Remote approvals
+        Dictionary<string, ToolApprovalRequestContent> trackedRequestApprovalToolCalls = new(); // Remote approvals
         for (int messageIndex = 0; messageIndex < messages.Count; messageIndex++)
         {
             var message = messages[messageIndex];
@@ -134,7 +146,7 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
                     });
                 }
                 else if (content is FunctionResultContent toolResult &&
-                    trackedRequestApprovalToolCalls.TryGetValue(toolResult.CallId, out var approval))
+                    trackedRequestApprovalToolCalls.TryGetValue(toolResult.CallId, out var approval) == true)
                 {
                     result ??= CopyMessagesUpToIndex(messages, messageIndex);
                     transformedContents ??= CopyContentsUpToIndex(message.Contents, j);
@@ -149,9 +161,9 @@ internal sealed class ServerFunctionApprovalAgent : DelegatingAIAgent
                         AdditionalProperties = message.AdditionalProperties
                     });
                 }
-                else
+                else if (result != null)
                 {
-                    result?.Add(message);
+                    result.Add(message);
                 }
             }
         }

dotnet/samples/02-agents/AGUI/Step05_StateManagement/Client/StatefulAgent.cs

@@ -72,9 +72,10 @@ internal sealed class StatefulAgent<TState> : DelegatingAIAgent
                 if (content is DataContent dataContent && dataContent.MediaType == "application/json")
                 {
                     // Deserialize the state
-                    if (JsonSerializer.Deserialize(
+                    TState? newState = JsonSerializer.Deserialize(
                         dataContent.Data.Span,
-                        this._jsonSerializerOptions.GetTypeInfo(typeof(TState))) is TState newState)
+                        this._jsonSerializerOptions.GetTypeInfo(typeof(TState))) as TState;
+                    if (newState != null)
                     {
                         this.State = newState;
                     }

dotnet/samples/02-agents/AgentOpenTelemetry/Program.cs

@@ -18,7 +18,6 @@ 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";
 
@@ -41,6 +40,7 @@ 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));
 
@@ -54,7 +54,8 @@ 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 source
+    .AddMeter(SourceName) // Our custom meter
+    .AddMeter("*Microsoft.Agents.AI") // Agent Framework metrics
     .AddHttpClientInstrumentation() // HTTP client metrics
     .AddRuntimeInstrumentation() // .NET runtime metrics
     .AddOtlpExporter(options => options.Endpoint = new Uri(otlpEndpoint))
@@ -127,7 +128,7 @@ var agent = new ChatClientAgent(instrumentedChatClient,
     instructions: "You are a helpful assistant that provides concise and informative responses.",
     tools: [AIFunctionFactory.Create(GetWeatherAsync)])
     .AsBuilder()
-    .UseOpenTelemetry(sourceName: SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) // enable telemetry at the agent level
+    .UseOpenTelemetry(SourceName, configure: (cfg) => cfg.EnableSensitiveData = true) // enable telemetry at the agent level
     .Build();
 
 var session = await agent.CreateSessionAsync();

dotnet/samples/02-agents/AgentProviders/Agent_With_AzureAIProject/Program.cs

@@ -6,7 +6,6 @@ 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";
@@ -31,18 +30,14 @@ var createdAgentVersion = aiProjectClient.Agents.CreateAgentVersion(agentName: J
 //      agentVersion.Name = <agentName>
 
 // You can use an AIAgent with an already created server side agent version.
-FoundryAgent existingJokerAgent = aiProjectClient.AsAIAgent(createdAgentVersion);
+AIAgent existingJokerAgent = aiProjectClient.AsAIAgent(createdAgentVersion);
 
 // You can also create another AIAgent version by providing the same name with a different definition.
-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);
+AIAgent newJokerAgent = await aiProjectClient.CreateAIAgentAsync(name: JokerName, model: deploymentName, instructions: "You are extremely hilarious at telling jokes.");
 
 // You can also get the AIAgent latest version just providing its name.
-AgentRecord jokerAgentRecord = await aiProjectClient.Agents.GetAgentAsync(JokerName);
-FoundryAgent jokerAgentLatest = aiProjectClient.AsAIAgent(jokerAgentRecord);
-AgentVersion latestAgentVersion = jokerAgentRecord.GetLatestVersion();
+AIAgent jokerAgentLatest = await aiProjectClient.GetAIAgentAsync(name: JokerName);
+var latestAgentVersion = jokerAgentLatest.GetService<AgentVersion>()!;
 
 // The AIAgent version can be accessed via the GetService method.
 Console.WriteLine($"Latest agent version id: {latestAgentVersion.Id}");

dotnet/samples/02-agents/AgentSkills/Agent_Step01_BasicSkills/Agent_Step01_BasicSkills.csproj

@@ -14,10 +14,6 @@
     <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>

dotnet/samples/02-agents/AgentSkills/Agent_Step01_BasicSkills/Program.cs

@@ -0,0 +1,50 @@
+// 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");

dotnet/samples/02-agents/AgentSkills/Agent_Step01_BasicSkills/README.md

@@ -0,0 +1,63 @@
+# 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/)

dotnet/samples/02-agents/AgentSkills/Agent_Step01_BasicSkills/skills/expense-report/SKILL.md

@@ -0,0 +1,40 @@
+---
+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.

dotnet/samples/02-agents/AgentSkills/Agent_Step01_BasicSkills/skills/expense-report/assets/expense-report-template.md

@@ -0,0 +1,5 @@
+# Expense Report Template
+
+| Date | Category | Vendor | Description | Amount (USD) | Original Currency | Original Amount | Attendees | Business Purpose | Receipt Attached |
+|------|----------|--------|-------------|--------------|-------------------|-----------------|-----------|------------------|------------------|
+|      |          |        |             |              |                   |                 |           |                  | Yes or No        |

dotnet/samples/02-agents/AgentSkills/Agent_Step01_BasicSkills/skills/expense-report/references/POLICY_FAQ.md

@@ -0,0 +1,55 @@
+# 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.

dotnet/samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/Program.cs

@@ -1,48 +0,0 @@
-// 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}");

dotnet/samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/README.md

@@ -1,51 +0,0 @@
-# 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**
-```

dotnet/samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/skills/unit-converter/SKILL.md

@@ -1,11 +0,0 @@
----
-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

dotnet/samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/skills/unit-converter/references/conversion-table.md

@@ -1,10 +0,0 @@
-# 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  |

dotnet/samples/02-agents/AgentSkills/Agent_Step01_FileBasedSkills/skills/unit-converter/scripts/convert.py

@@ -1,29 +0,0 @@
-# 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()

dotnet/samples/02-agents/AgentSkills/README.md

@@ -4,4 +4,4 @@ Samples demonstrating Agent Skills capabilities.
 
 | Sample | Description |
 |--------|-------------|
-| [Agent_Step01_FileBasedSkills](Agent_Step01_FileBasedSkills/) | Define skills as `SKILL.md` files on disk with reference documents. Uses a unit-converter skill. |
+| [Agent_Step01_BasicSkills](Agent_Step01_BasicSkills/) | Using Agent Skills with a ChatClientAgent, including progressive disclosure and skill resources |

dotnet/samples/02-agents/AgentSkills/SubprocessScriptRunner.cs

@@ -1,137 +0,0 @@
-// 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('-');
-}

dotnet/samples/02-agents/AgentWithAnthropic/Agent_Anthropic_Step01_Running/Program.cs

@@ -5,13 +5,20 @@
 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.
-Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
+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);
+}

dotnet/samples/02-agents/AgentWithMemory/AgentWithMemory_Step04_MemoryUsingFoundry/Program.cs

@@ -11,7 +11,6 @@ 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.");
@@ -20,9 +19,6 @@ 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);
 
@@ -37,15 +33,11 @@ FoundryMemoryProvider memoryProvider = new(
     memoryStoreName,
     stateInitializer: _ => new(new FoundryMemoryProviderScope("sample-user-123")));
 
-FoundryAgent agent = projectClient.AsAIAgent(
-    new ChatClientAgentOptions()
+AIAgent agent = await projectClient.CreateAIAgentAsync(deploymentName,
+    options: new ChatClientAgentOptions()
     {
         Name = "TravelAssistantWithFoundryMemory",
-        ChatOptions = new()
-        {
-            ModelId = deploymentName,
-            Instructions = "You are a friendly travel assistant. Use known memories about the user when responding, and do not invent details."
-        },
+        ChatOptions = new() { Instructions = "You are a friendly travel assistant. Use known memories about the user when responding, and do not invent details." },
         AIContextProviders = [memoryProvider]
     });
 

dotnet/samples/02-agents/AgentWithMemory/README.md

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

dotnet/samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step01_Running/Program.cs

@@ -4,14 +4,28 @@
 
 using System.ClientModel;
 using Microsoft.Agents.AI;
-using OpenAI.Responses;
+using OpenAI;
+using OpenAI.Chat;
 
 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 ResponsesClient(new ApiKeyCredential(apiKey))
-    .AsAIAgent(model: model, instructions: "You are good at telling jokes.", name: "Joker");
+AIAgent agent = new OpenAIClient(apiKey)
+    .GetChatClient(model)
+    .AsAIAgent(instructions: "You are good at telling jokes.", name: "Joker");
 
-// 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."));
+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);
+    }
+}

dotnet/samples/02-agents/AgentWithOpenAI/Agent_OpenAI_Step05_Conversation/Program.cs

@@ -73,28 +73,16 @@ 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 = roleElement.ToString();
+        string messageRole = element.GetProperty("role"u8).ToString();
         Console.WriteLine($"    Message ID: {messageId}");
         Console.WriteLine($"    Message Role: {messageRole}");
 
-        if (element.TryGetProperty("content"u8, out var contentElement))
+        foreach (var content in element.GetProperty("content").EnumerateArray())
         {
-            foreach (var content in contentElement.EnumerateArray())
-            {
-                if (content.TryGetProperty("text"u8, out var textElement))
-                {
-                    Console.WriteLine($"    Message Text: {textElement}");
-                }
-            }
+            string messageContentText = content.GetProperty("text"u8).ToString();
+            Console.WriteLine($"    Message Text: {messageContentText}");
         }
-
         Console.WriteLine();
     }
 }

dotnet/samples/02-agents/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/Program.cs

@@ -4,13 +4,11 @@
 
 using System.ClientModel;
 using Azure.AI.Projects;
-using Azure.AI.Projects.Agents;
 using Azure.Identity;
 using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.AzureAI;
+using Microsoft.Extensions.AI;
 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.");
@@ -39,20 +37,14 @@ ClientResult<VectorStore> vectorStoreCreate = await vectorStoreClient.CreateVect
     FileIds = { uploadResult.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
+var fileSearchTool = new HostedFileSearchTool() { Inputs = [new HostedVectorStoreContent(vectorStoreCreate.Value.Id)] };
 
-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);
+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]);
 
 AgentSession session = await agent.CreateSessionAsync();
 

dotnet/samples/02-agents/Agents/Agent_Step07_AsMcpTool/Program.cs

@@ -3,7 +3,6 @@
 // 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;
@@ -19,17 +18,11 @@ 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.
-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);
+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.");
 
 // 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.

dotnet/samples/02-agents/Agents/Agent_Step08_UsingImages/Agent_Step08_UsingImages.csproj

@@ -16,11 +16,5 @@
   <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>

dotnet/samples/02-agents/Agents/Agent_Step08_UsingImages/Program.cs

@@ -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?"),
-    await DataContent.LoadFromAsync("Assets/walkway.jpg"),
+    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")
 ]);
 
 var session = await agent.CreateSessionAsync();

dotnet/samples/02-agents/Agents/Agent_Step11_Middleware/Program.cs

@@ -189,9 +189,9 @@ async Task<AgentResponse> PIIMiddleware(IEnumerable<ChatMessage> messages, Agent
         // Regex patterns for PII detection (simplified for demonstration)
         Regex[] piiPatterns =
         [
-            MyRegex(), // Phone number (e.g., 123-456-7890)
-            EmailRegex(), // Email address
-            FullNameRegex() // Full name (e.g., John Doe)
+            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)
         ];
 
         foreach (var pattern in piiPatterns)
@@ -309,15 +309,3 @@ 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();
-}

dotnet/samples/02-agents/Agents/Agent_Step15_DeepResearch/Program.cs

@@ -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.

dotnet/samples/02-agents/Agents/Agent_Step15_DeepResearch/README.md

@@ -23,14 +23,12 @@ Before running this sample, ensure you have:
 
 Pay special attention to the purple `Note` boxes in the Azure documentation.
 
-**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:
+**Note**: The Bing Connection ID must be from the **project**, not the resource. It has the following format:
 
 ```
-/subscriptions/<sub-id>/resourceGroups/<rg>/providers/Microsoft.CognitiveServices/accounts/<account>/projects/<project>/connections/<connection-name>
+/subscriptions/<sub_id>/resourceGroups/<rg_name>/providers/<provider_name>/accounts/<account_name>/projects/<project_name>/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:
@@ -39,8 +37,8 @@ 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 Grounding connection ID (full ARM resource URI)
-$env:AZURE_AI_BING_CONNECTION_ID="/subscriptions/<sub-id>/resourceGroups/<rg>/providers/Microsoft.CognitiveServices/accounts/<account>/projects/<project>/connections/<connection-name>"
+# Replace with your Bing connection ID from the project
+$env:AZURE_AI_BING_CONNECTION_ID="/subscriptions/.../connections/your-bing-connection"
 
 # Optional, defaults to o3-deep-research
 $env:AZURE_AI_REASONING_DEPLOYMENT_NAME="o3-deep-research"

dotnet/samples/02-agents/Agents/Agent_Step17_AdditionalAIContext/Program.cs

@@ -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
-    [
+    return new string[]
+    {
         "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)) ?? [];
+            => session?.StateBag.GetValue<List<string>>(nameof(TodoListAIContextProvider)) ?? new List<string>();
 
         private static void SetTodoItems(AgentSession? session, List<string> items)
             => session?.StateBag.SetValue(nameof(TodoListAIContextProvider), items);

dotnet/samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing/Program.cs

@@ -1,226 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample demonstrates how the ChatClientAgent persists chat history after each individual
-// call to the AI service.
-// 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 opt into end-of-run persistence instead (atomic run semantics), set
-// PersistChatHistoryAtEndOfRun = true on ChatClientAgentOptions.
-//
-// 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 the default behavior.
-// 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",
-        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;
-    }
-}

dotnet/samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing/README.md

@@ -1,63 +0,0 @@
-# In-Function-Loop Checkpointing
-
-This sample demonstrates how `ChatClientAgent` persists chat history after each individual call to the AI service by default. 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 default, chat history is persisted after each service call via the `ChatHistoryPersistingChatClient` decorator:
-
-- A `ChatHistoryPersistingChatClient` decorator is automatically inserted into the chat client pipeline
-- 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
-
-To opt into end-of-run persistence instead (atomic run semantics), set `PersistChatHistoryAtEndOfRun = true` on `ChatClientAgentOptions`. In that mode, the decorator marks messages with metadata rather than persisting them immediately, and `ChatClientAgent` persists only the marked messages at the end of the run.
-
-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)
-       └─ ChatHistoryPersistingChatClient  (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.

dotnet/samples/02-agents/Agents/README.md

@@ -45,7 +45,6 @@ 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
 

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step00_FoundryAgentLifecycle/Program.cs

@@ -1,36 +0,0 @@
-// 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);

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step00_FoundryAgentLifecycle/README.md

@@ -1,23 +0,0 @@
-# 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
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step01_Basics/Agent_Step01_Basics.csproj

@@ -1,15 +0,0 @@
-<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>

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step01_Basics/Program.cs

@@ -1,20 +0,0 @@
-// 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."));

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step01_Basics/README.md

@@ -1,55 +0,0 @@
-# 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.

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step02.1_MultiturnConversation/Program.cs

@@ -1,26 +0,0 @@
-// 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));

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step02.1_MultiturnConversation/README.md

@@ -1,36 +0,0 @@
-# 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
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step02.2_MultiturnWithServerConversations/Agent_Step02.2_MultiturnWithServerConversations.csproj

@@ -1,15 +0,0 @@
-<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>

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step02.2_MultiturnWithServerConversations/Program.cs

@@ -1,34 +0,0 @@
-// 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();

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step02.2_MultiturnWithServerConversations/README.md

@@ -1,36 +0,0 @@
-# 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
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step03_UsingFunctionTools/Agent_Step03_UsingFunctionTools.csproj

@@ -1,15 +0,0 @@
-<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>

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step03_UsingFunctionTools/Program.cs

@@ -1,41 +0,0 @@
-// 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);
-}

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step03_UsingFunctionTools/README.md

@@ -1,37 +0,0 @@
-# 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
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step04_UsingFunctionToolsWithApprovals/Agent_Step04_UsingFunctionToolsWithApprovals.csproj

@@ -1,15 +0,0 @@
-<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>

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step04_UsingFunctionToolsWithApprovals/README.md

@@ -1,30 +0,0 @@
-# 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
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step05_StructuredOutput/Agent_Step05_StructuredOutput.csproj

@@ -1,15 +0,0 @@
-<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>

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step05_StructuredOutput/README.md

@@ -1,29 +0,0 @@
-# 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
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step06_PersistedConversations/Agent_Step06_PersistedConversations.csproj

@@ -1,15 +0,0 @@
-<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>

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step06_PersistedConversations/README.md

@@ -1,30 +0,0 @@
-# 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
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step07_Observability/README.md

@@ -1,31 +0,0 @@
-# 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
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step08_DependencyInjection/Program.cs

@@ -1,83 +0,0 @@
-// 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;
-        }
-    }
-}

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step08_DependencyInjection/README.md

@@ -1,30 +0,0 @@
-# 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
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step09_UsingMcpClientAsTools/Program.cs

@@ -1,44 +0,0 @@
-// 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)}");

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step09_UsingMcpClientAsTools/README.md

@@ -1,29 +0,0 @@
-# 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
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step10_UsingImages/Agent_Step10_UsingImages.csproj

@@ -1,21 +0,0 @@
-<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>

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step10_UsingImages/README.md

@@ -1,30 +0,0 @@
-# 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
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step11_AsFunctionTool/Agent_Step11_AsFunctionTool.csproj

@@ -1,15 +0,0 @@
-<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>

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step11_AsFunctionTool/README.md

@@ -1,30 +0,0 @@
-# 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
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step12_Middleware/README.md

@@ -1,31 +0,0 @@
-# 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
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step13_Plugins/Program.cs

@@ -1,153 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to use plugins with an AI agent. Plugin classes can
-// depend on other services that need to be injected. In this sample, the
-// AgentPlugin class uses the WeatherProvider and CurrentTimeProvider classes
-// to get weather and current time information. Both services are registered
-// in the service collection and injected into the plugin.
-// Plugin classes may have many methods, but only some are intended to be used
-// as AI functions. The AsAITools method of the plugin class shows how to specify
-// which methods should be exposed to the AI agent.
-
-using Azure.AI.Projects;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-using Microsoft.Extensions.DependencyInjection;
-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 helps people find information.";
-const string AssistantName = "PluginAssistant";
-
-// Create a service collection to hold the agent plugin and its dependencies.
-ServiceCollection services = new();
-services.AddSingleton<WeatherProvider>();
-services.AddSingleton<CurrentTimeProvider>();
-services.AddSingleton<AgentPlugin>(); // The plugin depends on WeatherProvider and CurrentTimeProvider registered above.
-
-IServiceProvider serviceProvider = services.BuildServiceProvider();
-
-// 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 ChatClientAgent with the options-based constructor to pass services.
-AIAgent agent = aiProjectClient.AsAIAgent(new ChatClientAgentOptions
-{
-    Name = AssistantName,
-    ChatOptions = new() { ModelId = deploymentName, Instructions = AssistantInstructions, Tools = serviceProvider.GetRequiredService<AgentPlugin>().AsAITools().ToList() }
-},
-    services: serviceProvider);
-
-// Invoke the agent and output the text result.
-AgentSession session = await agent.CreateSessionAsync();
-Console.WriteLine(await agent.RunAsync("Tell me current time and weather in Seattle.", session));
-
-namespace SampleApp
-{
-    /// <summary>
-    /// The agent plugin that provides weather and current time information.
-    /// </summary>
-    internal sealed class AgentPlugin
-    {
-        private readonly WeatherProvider _weatherProvider;
-
-        /// <summary>
-        /// Initializes a new instance of the <see cref="AgentPlugin"/> class.
-        /// </summary>
-        /// <param name="weatherProvider">The weather provider to get weather information.</param>
-        public AgentPlugin(WeatherProvider weatherProvider)
-        {
-            this._weatherProvider = weatherProvider;
-        }
-
-        /// <summary>
-        /// Gets the weather information for the specified location.
-        /// </summary>
-        /// <remarks>
-        /// This method demonstrates how to use the dependency that was injected into the plugin class.
-        /// </remarks>
-        /// <param name="location">The location to get the weather for.</param>
-        /// <returns>The weather information for the specified location.</returns>
-        public string GetWeather(string location)
-        {
-            return this._weatherProvider.GetWeather(location);
-        }
-
-        /// <summary>
-        /// Gets the current date and time for the specified location.
-        /// </summary>
-        /// <remarks>
-        /// This method demonstrates how to resolve a dependency using the service provider passed to the method.
-        /// </remarks>
-        /// <param name="sp">The service provider to resolve the <see cref="CurrentTimeProvider"/>.</param>
-        /// <param name="location">The location to get the current time for.</param>
-        /// <returns>The current date and time as a <see cref="DateTimeOffset"/>.</returns>
-        public DateTimeOffset GetCurrentTime(IServiceProvider sp, string location)
-        {
-            CurrentTimeProvider currentTimeProvider = sp.GetRequiredService<CurrentTimeProvider>();
-            return currentTimeProvider.GetCurrentTime(location);
-        }
-
-        /// <summary>
-        /// Returns the functions provided by this plugin.
-        /// </summary>
-        /// <remarks>
-        /// In real world scenarios, a class may have many methods and only a subset of them may be intended to be exposed as AI functions.
-        /// This method demonstrates how to explicitly specify which methods should be exposed to the AI agent.
-        /// </remarks>
-        /// <returns>The functions provided by this plugin.</returns>
-        public IEnumerable<AITool> AsAITools()
-        {
-            yield return AIFunctionFactory.Create(this.GetWeather);
-            yield return AIFunctionFactory.Create(this.GetCurrentTime);
-        }
-    }
-
-    internal sealed class WeatherProvider
-    {
-        private readonly string _weatherSummary = "cloudy with a high of 15°C";
-
-        /// <summary>
-        /// The weather provider that returns weather information.
-        /// </summary>
-        /// <summary>
-        /// Gets the weather information for the specified location.
-        /// </summary>
-        /// <remarks>
-        /// The weather information is hardcoded for demonstration purposes.
-        /// In a real application, this could call a weather API to get actual weather data.
-        /// </remarks>
-        /// <param name="location">The location to get the weather for.</param>
-        /// <returns>The weather information for the specified location.</returns>
-        public string GetWeather(string location)
-        {
-            return $"The weather in {location} is {this._weatherSummary}.";
-        }
-    }
-
-    internal sealed class CurrentTimeProvider
-    {
-        private readonly TimeProvider _timeProvider = TimeProvider.System;
-
-        /// <summary>
-        /// Provides the current date and time.
-        /// </summary>
-        /// <remarks>
-        /// This class returns the current date and time using the system's clock.
-        /// </remarks>
-        /// <summary>
-        /// Gets the current date and time.
-        /// </summary>
-        /// <param name="location">The location to get the current time for (not used in this implementation).</param>
-        /// <returns>The current date and time as a <see cref="DateTimeOffset"/>.</returns>
-        public DateTimeOffset GetCurrentTime(string location)
-        {
-            return this._timeProvider.GetLocalNow();
-        }
-    }
-}

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step13_Plugins/README.md

@@ -1,29 +0,0 @@
-# Using Plugins with the Responses API
-
-This sample shows how to use plugins with a `ChatClientAgent` using the Responses API directly, with dependency injection for plugin services.
-
-## What this sample demonstrates
-
-- Creating plugin classes with injected dependencies
-- Registering services and building a service provider
-- Passing `services` to the `ChatClientAgent` via the options-based constructor
-- Using `AIFunctionFactory` to expose plugin methods as AI tools
-
-## 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
-dotnet run
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step14_CodeInterpreter/README.md

@@ -1,28 +0,0 @@
-# Code Interpreter with the Responses API
-
-This sample shows how to use the Code Interpreter tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
-- Using `HostedCodeInterpreterTool` with `ChatClientAgent`
-- Extracting code input and output from agent responses
-- Handling code interpreter annotations and file citations
-
-## 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
-dotnet run
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step15_ComputerUse/README.md

@@ -1,29 +0,0 @@
-# Computer Use with the Responses API
-
-This sample shows how to use the Computer Use tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
-- Using `FoundryAITool.CreateComputerTool()` with `ChatClientAgent`
-- Processing computer call actions (click, type, key press)
-- Managing the computer use interaction loop with screenshots
-- Handling the Azure Agents API workaround for `previous_response_id` with `computer_call_output`
-
-## 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="computer-use-preview"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step16_FileSearch/README.md

@@ -1,29 +0,0 @@
-# File Search with the Responses API
-
-This sample shows how to use the File Search tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
-- Uploading files and creating vector stores via `AIProjectClient`
-- Using `HostedFileSearchTool` with `ChatClientAgent`
-- Handling file citation annotations in agent responses
-- Cleaning up file resources after use
-
-## 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
-dotnet run
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step17_OpenAPITools/Agent_Step17_OpenAPITools.csproj

@@ -1,20 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.Identity" />
-  </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>
-
-</Project>

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step17_OpenAPITools/README.md

@@ -1,29 +0,0 @@
-# OpenAPI Tools with the Responses API
-
-This sample shows how to use OpenAPI tools with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
-- Defining an OpenAPI specification inline
-- Creating an `OpenAPIFunctionDefinition` for the REST Countries API
-- Using `FoundryAITool.CreateOpenApiTool()` with `ChatClientAgent`
-- Server-side execution of OpenAPI tool calls
-
-## 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
-dotnet run
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step18_BingCustomSearch/README.md

@@ -1,36 +0,0 @@
-# Bing Custom Search with the Responses API
-
-This sample shows how to use the Bing Custom Search tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
-- Configuring `BingCustomSearchToolParameters` with connection ID and instance name
-- Using `FoundryAITool.CreateBingCustomSearchTool()` with `ChatClientAgent`
-- Processing search results from agent responses
-
-## Prerequisites
-
-- .NET 10 SDK or later
-- Microsoft Foundry service endpoint and deployment configured
-- Azure CLI installed and authenticated (`az login`)
-- Bing Custom Search resource configured with a connection ID
-
-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"
-$env:AZURE_AI_CUSTOM_SEARCH_CONNECTION_ID="your-connection-id"  # The full ARM resource URI, e.g., "/subscriptions/.../connections/your-bing-connection"
-$env:AZURE_AI_CUSTOM_SEARCH_INSTANCE_NAME="your-instance-name"  # The Bing Custom Search configuration name (from Azure portal)
-```
-
-### Finding the connection ID and instance name
-
-- **Connection ID** (`AZURE_AI_CUSTOM_SEARCH_CONNECTION_ID`): The full ARM resource URI including the `/projects/<name>/connections/<connection-name>` segment. Find the connection name in your Foundry project under **Management center** → **Connected resources**.
-- **Instance Name** (`AZURE_AI_CUSTOM_SEARCH_INSTANCE_NAME`): The **configuration name** from your Bing Custom Search resource (Azure portal → your Bing Custom Search resource → **Configurations**). This is _not_ the Azure resource name or the connection name — it's the name of the specific search configuration that defines which domains/sites to search against.
-
-## Run the sample
-
-```powershell
-dotnet run
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step19_SharePoint/Agent_Step19_SharePoint.csproj

@@ -1,19 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step19_SharePoint/README.md

@@ -1,30 +0,0 @@
-# SharePoint Grounding with the Responses API
-
-This sample shows how to use the SharePoint Grounding tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
-- Configuring `SharePointGroundingToolOptions` with project connections
-- Using `FoundryAITool.CreateSharepointTool()` with `ChatClientAgent`
-- Displaying grounding annotations from agent responses
-
-## Prerequisites
-
-- .NET 10 SDK or later
-- Microsoft Foundry service endpoint and deployment configured
-- Azure CLI installed and authenticated (`az login`)
-- SharePoint connection configured in your Microsoft Foundry project
-
-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:SHAREPOINT_PROJECT_CONNECTION_ID="your-sharepoint-connection-id"  # The full ARM resource URI, e.g., "/subscriptions/.../connections/SharepointTestTool"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step20_MicrosoftFabric/Agent_Step20_MicrosoftFabric.csproj

@@ -1,19 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step20_MicrosoftFabric/Program.cs

@@ -1,42 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to use Microsoft Fabric Tool with a ChatClientAgent.
-
-using Azure.AI.Projects;
-using Azure.AI.Projects.Agents;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.AzureAI;
-
-string fabricConnectionId = Environment.GetEnvironmentVariable("FABRIC_PROJECT_CONNECTION_ID") ?? throw new InvalidOperationException("FABRIC_PROJECT_CONNECTION_ID is not set.");
-
-const string AgentInstructions = "You are a helpful assistant with access to Microsoft Fabric data. Answer questions based on data available through your Fabric connection.";
-
-// Configure Microsoft Fabric tool options with project connection
-var fabricToolOptions = new FabricDataAgentToolOptions();
-fabricToolOptions.ProjectConnections.Add(new ToolProjectConnection(fabricConnectionId));
-
-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 Microsoft Fabric tool.
-AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
-    instructions: AgentInstructions,
-    name: "FabricAgent-RAPI",
-    tools: [FoundryAITool.CreateMicrosoftFabricTool(fabricToolOptions)]);
-
-Console.WriteLine($"Created agent: {agent.Name}");
-
-// Run the agent with a sample query
-AgentResponse response = await agent.RunAsync("What data is available in the connected Fabric workspace?");
-
-Console.WriteLine("\n=== Agent Response ===");
-foreach (var message in response.Messages)
-{
-    Console.WriteLine(message.Text);
-}

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step20_MicrosoftFabric/README.md

@@ -1,30 +0,0 @@
-# Microsoft Fabric with the Responses API
-
-This sample shows how to use the Microsoft Fabric tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
-- Configuring `FabricDataAgentToolOptions` with project connections
-- Using `FoundryAITool.CreateMicrosoftFabricTool()` with `ChatClientAgent`
-- Querying data available through a Fabric connection
-
-## Prerequisites
-
-- .NET 10 SDK or later
-- Microsoft Foundry service endpoint and deployment configured
-- Azure CLI installed and authenticated (`az login`)
-- Microsoft Fabric connection configured in your Microsoft Foundry project
-
-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:FABRIC_PROJECT_CONNECTION_ID="your-fabric-connection-id"  # The full ARM resource URI, e.g., "/subscriptions/.../connections/FabricTestTool"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step21_WebSearch/Agent_Step21_WebSearch.csproj

@@ -1,19 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step21_WebSearch/Program.cs

@@ -1,44 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to use the Web Search Tool with a ChatClientAgent.
-
-using Azure.AI.Projects;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-using OpenAI.Responses;
-
-const string AgentInstructions = "You are a helpful assistant that can search the web to find current information and answer questions accurately.";
-const string AgentName = "WebSearchAgent-RAPI";
-
-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 HostedWebSearchTool.
-AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
-    instructions: AgentInstructions,
-    name: AgentName,
-    tools: [new HostedWebSearchTool()]);
-
-AgentResponse response = await agent.RunAsync("What's the weather today in Seattle?");
-
-// Get the text response
-Console.WriteLine($"Response: {response.Text}");
-
-// Getting any annotations/citations generated by the web search tool
-foreach (AIAnnotation annotation in response.Messages.SelectMany(m => m.Contents).SelectMany(c => c.Annotations ?? []))
-{
-    Console.WriteLine($"Annotation: {annotation}");
-    if (annotation.RawRepresentation is UriCitationMessageAnnotation urlCitation)
-    {
-        Console.WriteLine($$"""
-            Title: {{urlCitation.Title}}
-            URL: {{urlCitation.Uri}}
-            """);
-    }
-}

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step21_WebSearch/README.md

@@ -1,28 +0,0 @@
-# Web Search with the Responses API
-
-This sample shows how to use the Web Search tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
-- Using `HostedWebSearchTool` with `ChatClientAgent`
-- Processing web search citations and annotations
-- Extracting URL citation details (title, URL) from responses
-
-## 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
-dotnet run
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step22_MemorySearch/Agent_Step22_MemorySearch.csproj

@@ -1,20 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.Identity" />
-  </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>
-
-</Project>

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step22_MemorySearch/README.md

@@ -1,31 +0,0 @@
-# Memory Search with the Responses API
-
-This sample demonstrates how to use the Memory Search tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
-- Configuring `MemorySearchPreviewTool` with a memory store and user scope
-- Using memory search for cross-conversation recall
-- Inspecting `MemorySearchToolCallResponseItem` results
-- User profile persistence across conversations
-
-## Prerequisites
-
-- .NET 10 SDK or later
-- Microsoft Foundry service endpoint and deployment configured
-- Azure CLI installed and authenticated (`az login`)
-- A memory store created beforehand via Azure Portal or Python SDK
-
-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:AZURE_AI_MEMORY_STORE_ID="your-memory-store-name"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```

dotnet/samples/02-agents/AgentsWithFoundry/Agent_Step23_LocalMCP/README.md

@@ -1,29 +0,0 @@
-# Local MCP with the Responses API
-
-This sample demonstrates how to use a local MCP (Model Context Protocol) client with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
-- Connecting to an MCP server via HTTP (Streamable HTTP transport)
-- Resolving MCP tools locally and wrapping them with logging
-- Using `DelegatingAIFunction` to add custom behavior to MCP tools
-- Passing locally-resolved MCP tools to `ChatClientAgent`
-
-## 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
-dotnet run
-```

dotnet/samples/02-agents/AgentsWithFoundry/README.md

@@ -1,81 +0,0 @@
-# Getting started with Foundry Agents
-
-These samples demonstrate how to use Azure AI Foundry with Agent Framework.
-
-## Quick start
-
-The simplest way to create a Foundry agent is using the `FoundryAgent` type directly:
-
-```csharp
-FoundryAgent agent = new(
-    new Uri(endpoint),
-    new AzureCliCredential(),
-    model: "gpt-4o-mini",
-    instructions: "You are good at telling jokes.",
-    name: "JokerAgent");
-
-Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
-```
-
-Or using the `AIProjectClient.AsAIAgent(...)` extensions:
-
-```csharp
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
-
-FoundryAgent agent = aiProjectClient.AsAIAgent(
-    model: deploymentName,
-    instructions: "You are good at telling jokes.",
-    name: "JokerAgent");
-```
-
-## Prerequisites
-
-- .NET 10 SDK or later
-- Foundry project endpoint
-- Azure CLI installed and authenticated
-
-Set:
-
-```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"
-```
-
-Some samples require extra tool-specific environment variables. See each sample for details.
-
-## Samples
-
-| Sample | Description |
-| --- | --- |
-| [FoundryAgent lifecycle](./Agent_Step00_FoundryAgentLifecycle/) | Create a FoundryAgent directly with endpoint and credentials |
-| [Basics (Responses API)](./Agent_Step01_Basics/) | Create and run an agent using AsAIAgent extensions |
-| [Multi-turn conversation](./Agent_Step02.1_MultiturnConversation/) | Multi-turn using sessions and response ID chaining |
-| [Multi-turn with server conversations](./Agent_Step02.2_MultiturnWithServerConversations/) | Server-side conversations visible in Foundry UI |
-| [Using function tools](./Agent_Step03_UsingFunctionTools/) | Function tools |
-| [Function tools with approvals](./Agent_Step04_UsingFunctionToolsWithApprovals/) | Human-in-the-loop approval |
-| [Structured output](./Agent_Step05_StructuredOutput/) | Structured output with JSON schema |
-| [Persisted conversations](./Agent_Step06_PersistedConversations/) | Persisting and resuming conversations |
-| [Observability](./Agent_Step07_Observability/) | OpenTelemetry observability |
-| [Dependency injection](./Agent_Step08_DependencyInjection/) | DI with a hosted service |
-| [Using MCP client as tools](./Agent_Step09_UsingMcpClientAsTools/) | MCP client tools |
-| [Using images](./Agent_Step10_UsingImages/) | Image multi-modality |
-| [Agent as function tool](./Agent_Step11_AsFunctionTool/) | Agent as a function tool for another |
-| [Middleware](./Agent_Step12_Middleware/) | Multiple middleware layers |
-| [Plugins](./Agent_Step13_Plugins/) | Plugins with dependency injection |
-| [Code interpreter](./Agent_Step14_CodeInterpreter/) | Code interpreter tool |
-| [Computer use](./Agent_Step15_ComputerUse/) | Computer use tool |
-| [File search](./Agent_Step16_FileSearch/) | File search tool |
-| [OpenAPI tools](./Agent_Step17_OpenAPITools/) | OpenAPI tools |
-| [Bing custom search](./Agent_Step18_BingCustomSearch/) | Bing Custom Search tool |
-| [SharePoint](./Agent_Step19_SharePoint/) | SharePoint grounding tool |
-| [Microsoft Fabric](./Agent_Step20_MicrosoftFabric/) | Microsoft Fabric tool |
-| [Web search](./Agent_Step21_WebSearch/) | Web search tool |
-| [Memory search](./Agent_Step22_MemorySearch/) | Memory search tool |
-| [Local MCP](./Agent_Step23_LocalMCP/) | Local MCP client with HTTP transport |
-
-## Running the samples
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\FoundryAgent_Step01
-```

dotnet/samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step01_RedTeaming/FoundryAgents_Evaluations_Step01_RedTeaming.csproj

@@ -9,7 +9,8 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+    <PackageReference Include="Azure.AI.Projects" />
+    <PackageReference Include="Azure.Identity" />
   </ItemGroup>
 
 </Project>

dotnet/samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step01_RedTeaming/Program.cs

@@ -0,0 +1,100 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use Azure AI Foundry's Red Teaming service to assess
+// the safety and resilience of an AI model against adversarial attacks.
+//
+// It uses the RedTeam API from Azure.AI.Projects to run automated attack simulations
+// with various attack strategies (encoding, obfuscation, jailbreaks) across multiple
+// risk categories (Violence, HateUnfairness, Sexual, SelfHarm).
+//
+// For more details, see:
+// https://learn.microsoft.com/azure/ai-foundry/concepts/ai-red-teaming-agent
+
+using Azure.AI.Projects;
+using Azure.Identity;
+
+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";
+
+Console.WriteLine("=" + new string('=', 79));
+Console.WriteLine("RED TEAMING EVALUATION SAMPLE");
+Console.WriteLine("=" + new string('=', 79));
+Console.WriteLine();
+
+// Initialize Azure credentials and clients
+// 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 aiProjectClient = new(new Uri(endpoint), credential);
+
+// Configure the target model for red teaming
+AzureOpenAIModelConfiguration targetConfig = new(deploymentName);
+
+// Create the red team run configuration
+RedTeam redTeamConfig = new(targetConfig)
+{
+    DisplayName = "FinancialAdvisor-RedTeam",
+    ApplicationScenario = "A financial advisor assistant that provides general financial advice and information.",
+    NumTurns = 3,
+    RiskCategories =
+    {
+        RiskCategory.Violence,
+        RiskCategory.HateUnfairness,
+        RiskCategory.Sexual,
+        RiskCategory.SelfHarm,
+    },
+    AttackStrategies =
+    {
+        AttackStrategy.Easy,
+        AttackStrategy.Moderate,
+        AttackStrategy.Jailbreak,
+    },
+};
+
+Console.WriteLine($"Target model: {deploymentName}");
+Console.WriteLine("Risk categories: Violence, HateUnfairness, Sexual, SelfHarm");
+Console.WriteLine("Attack strategies: Easy, Moderate, Jailbreak");
+Console.WriteLine($"Simulation turns: {redTeamConfig.NumTurns}");
+Console.WriteLine();
+
+// Submit the red team run to the service
+Console.WriteLine("Submitting red team run...");
+RedTeam redTeamRun = await aiProjectClient.RedTeams.CreateAsync(redTeamConfig, options: null);
+
+Console.WriteLine($"Red team run created: {redTeamRun.Name}");
+Console.WriteLine($"Status: {redTeamRun.Status}");
+Console.WriteLine();
+
+// Poll for completion
+Console.WriteLine("Waiting for red team run to complete (this may take several minutes)...");
+while (redTeamRun.Status != "Completed" && redTeamRun.Status != "Failed" && redTeamRun.Status != "Canceled")
+{
+    await Task.Delay(TimeSpan.FromSeconds(15));
+    redTeamRun = await aiProjectClient.RedTeams.GetAsync(redTeamRun.Name);
+    Console.WriteLine($"  Status: {redTeamRun.Status}");
+}
+
+Console.WriteLine();
+
+if (redTeamRun.Status == "Completed")
+{
+    Console.WriteLine("Red team run completed successfully!");
+    Console.WriteLine();
+    Console.WriteLine("Results:");
+    Console.WriteLine(new string('-', 80));
+    Console.WriteLine($"  Run name:    {redTeamRun.Name}");
+    Console.WriteLine($"  Display name: {redTeamRun.DisplayName}");
+    Console.WriteLine($"  Status:      {redTeamRun.Status}");
+
+    Console.WriteLine();
+    Console.WriteLine("Review the detailed results in the Azure AI Foundry portal:");
+    Console.WriteLine($"  {endpoint}");
+}
+else
+{
+    Console.WriteLine($"Red team run ended with status: {redTeamRun.Status}");
+}
+
+Console.WriteLine();
+Console.WriteLine(new string('=', 80));

dotnet/samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step01_RedTeaming/README.md

@@ -0,0 +1,101 @@
+# Red Teaming with Azure AI Foundry (Classic)
+
+> [!IMPORTANT]
+> This sample uses the **classic Azure AI Foundry** red teaming API (`/redTeams/runs`) via `Azure.AI.Projects`. Results are viewable in the classic Foundry portal experience. The **new Foundry** portal's red teaming feature uses a different evaluation-based API that is not yet available in the .NET SDK.
+
+This sample demonstrates how to use Azure AI Foundry's Red Teaming service to assess the safety and resilience of an AI model against adversarial attacks.
+
+## What this sample demonstrates
+
+- Configuring a red team run targeting an Azure OpenAI model deployment
+- Using multiple `AttackStrategy` options (Easy, Moderate, Jailbreak)
+- Evaluating across `RiskCategory` categories (Violence, HateUnfairness, Sexual, SelfHarm)
+- Submitting a red team scan and polling for completion
+- Reviewing results in the Azure AI Foundry portal
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure AI Foundry project (hub and project created)
+- Azure OpenAI deployment (e.g., gpt-4o or gpt-4o-mini)
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+### Regional Requirements
+
+Red teaming is only available in regions that support risk and safety evaluators:
+- **East US 2**, **Sweden Central**, **US North Central**, **France Central**, **Switzerland West**
+
+### Environment Variables
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/api/projects/your-project" # Replace with your Azure Foundry project endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+```
+
+## Run the sample
+
+Navigate to the sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step01_RedTeaming
+dotnet run
+```
+
+## Expected behavior
+
+The sample will:
+
+1. Configure a `RedTeam` run targeting the specified model deployment
+2. Define risk categories and attack strategies
+3. Submit the scan to Azure AI Foundry's Red Teaming service
+4. Poll for completion (this may take several minutes)
+5. Display the run status and direct you to the Azure AI Foundry portal for detailed results
+
+## Understanding Red Teaming
+
+### Attack Strategies
+
+| Strategy | Description |
+|----------|-------------|
+| Easy | Simple encoding/obfuscation attacks (ROT13, Leetspeak, etc.) |
+| Moderate | Moderate complexity attacks requiring an LLM for orchestration |
+| Jailbreak | Crafted prompts designed to bypass AI safeguards (UPIA) |
+
+### Risk Categories
+
+| Category | Description |
+|----------|-------------|
+| Violence | Content related to violence |
+| HateUnfairness | Hate speech or unfair content |
+| Sexual | Sexual content |
+| SelfHarm | Self-harm related content |
+
+### Interpreting Results
+
+- Results are available in the Azure AI Foundry portal (**classic view** — toggle at top-right) under the red teaming section
+- Lower Attack Success Rate (ASR) is better — target ASR < 5% for production
+- Review individual attack conversations to understand vulnerabilities
+
+### Current Limitations
+
+> [!NOTE]
+> - The .NET Red Teaming API (`Azure.AI.Projects`) currently supports targeting **model deployments only** via `AzureOpenAIModelConfiguration`. The `AzureAIAgentTarget` type exists in the SDK but is consumed by the **Evaluation Taxonomy** API (`/evaluationtaxonomies`), not by the Red Teaming API (`/redTeams/runs`).
+> - Agent-targeted red teaming with agent-specific risk categories (Prohibited actions, Sensitive data leakage, Task adherence) is documented in the [concept docs](https://learn.microsoft.com/azure/ai-foundry/concepts/ai-red-teaming-agent) but is not yet available via the public REST API or .NET SDK.
+> - Results from this API appear in the **classic** Azure AI Foundry portal view. The new Foundry portal uses a separate evaluation-based system with `eval_*` identifiers.
+
+## Related Resources
+
+- [Azure AI Red Teaming Agent](https://learn.microsoft.com/azure/ai-foundry/concepts/ai-red-teaming-agent)
+- [RedTeam .NET API Reference](https://learn.microsoft.com/dotnet/api/azure.ai.projects.redteam?view=azure-dotnet-preview)
+- [Risk and Safety Evaluations](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-metrics-built-in#risk-and-safety-evaluators)
+
+## Next Steps
+
+After running red teaming:
+1. Review attack results and strengthen agent guardrails
+2. Explore the Self-Reflection sample (FoundryAgents_Evaluations_Step02_SelfReflection) for quality assessment
+3. Set up continuous red teaming in your CI/CD pipeline

dotnet/samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step02_SelfReflection/FoundryAgents_Evaluations_Step02_SelfReflection.csproj

@@ -10,11 +10,16 @@
 
   <ItemGroup>
     <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.AI.Projects" />
     <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.Evaluation" />
+    <PackageReference Include="Microsoft.Extensions.AI.Evaluation.Quality" />
+    <PackageReference Include="Microsoft.Extensions.AI.Evaluation.Safety" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
   </ItemGroup>
 
 </Project>