fix: update PyRIT repository link from Azure/PyRIT to microsoft/PyRIT

Agent-Logs-Url: https://github.com/microsoft/agent-framework/sessions/830b8ccf-a79c-49b6-90c9-3bb3e740bc06 Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com>
Initial plan
2026-06-16 21:04:09 +08:00 · 2026-03-28 15:43:29 +00:00 · 2026-03-28 15:42:25 +00:00
540 changed files with 25140 additions and 18614 deletions
@@ -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
@@ -126,6 +126,8 @@ jobs:
          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
          -m integration
          -n logical --dist worksteal
          --timeout=120 --session-timeout=900 --timeout_method thread
@@ -286,6 +288,7 @@ jobs:
        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
@@ -62,7 +62,9 @@ jobs:
            azure:
              - 'python/packages/openai/**'
              - 'python/packages/core/agent_framework/azure/**'
-              - 'python/samples/**/providers/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'
            misc:
              - 'python/packages/anthropic/**'
              - 'python/packages/ollama/**'
@@ -221,6 +223,8 @@ jobs:
          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
          -m integration
          -n logical --dist worksteal
          --timeout=120 --session-timeout=900 --timeout_method thread
@@ -426,6 +430,7 @@ jobs:
        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
@@ -23,8 +23,10 @@ jobs:
    environment: integration
    env:
      # Required configuration for get-started samples
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
+      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
+      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
    defaults:
      run:
        working-directory: python
@@ -41,8 +43,10 @@ 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=$AZURE_AI_PROJECT_ENDPOINT" >> .env
+          echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
+          echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env

      - name: Run sample validation
        run: |
@@ -60,13 +64,14 @@ jobs:
    runs-on: ubuntu-latest
    environment: integration
    env:
-      # Foundry configuration
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      # Azure AI configuration
+      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
+      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # Azure OpenAI configuration
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME || vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
+      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
+      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
      # OpenAI configuration
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
@@ -92,10 +97,11 @@ 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=$AZURE_OPENAI_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env
          echo "AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=$AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME" >> .env
          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
@@ -119,7 +125,6 @@ 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: ${{ vars.OPENAI__RESPONSESMODELID }}
    defaults:
@@ -139,7 +144,6 @@ jobs:
      - name: Create .env for samples
        run: |
          echo "OPENAI_API_KEY=$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

@@ -154,14 +158,15 @@ 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:
+      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_API_VERSION: ${{ vars.AZURE_OPENAI_API_VERSION || '' }}
+      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
+      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
    defaults:
      run:
        working-directory: python
@@ -178,19 +183,100 @@ jobs:

      - name: Create .env for samples
        run: |
+          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
-          echo "AZURE_OPENAI_API_VERSION=$AZURE_OPENAI_API_VERSION" >> .env
+          echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env

      - name: Run sample validation
        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure --save-report --report-name 02-agents-azure
+          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:
@@ -323,16 +409,11 @@ jobs:
          name: validation-report-02-agents-ollama
          path: python/samples/sample_validation/reports/

-  validate-02-agents-foundry:
-    name: Validate 02-agents/providers/foundry
-    if: false # Temporarily disabled - provider folder also contains the local Foundry sample
+  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 || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      FOUNDRY_AGENT_NAME: ${{ vars.FOUNDRY_AGENT_NAME || '' }}
-      FOUNDRY_AGENT_VERSION: ${{ vars.FOUNDRY_AGENT_VERSION || '' }}
    defaults:
      run:
        working-directory: python
@@ -347,22 +428,15 @@ jobs:
          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
          os: ${{ runner.os }}

-      - name: Create .env for samples
-        run: |
-          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
-          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
-          echo "FOUNDRY_AGENT_NAME=$FOUNDRY_AGENT_NAME" >> .env
-          echo "FOUNDRY_AGENT_VERSION=$FOUNDRY_AGENT_VERSION" >> .env
-
      - name: Run sample validation
        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/foundry --save-report --report-name 02-agents-foundry
+          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:
@@ -441,8 +515,13 @@ jobs:
    runs-on: ubuntu-latest
    environment: integration
    env:
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      # Azure AI configuration
+      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
+      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      # Azure OpenAI configuration
+      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
+      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
+      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
    defaults:
      run:
        working-directory: python
@@ -459,8 +538,11 @@ 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=$AZURE_OPENAI_ENDPOINT" >> .env
+          echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env

      - name: Run sample validation
        run: |
@@ -479,8 +561,12 @@ jobs:
    runs-on: ubuntu-latest
    environment: integration
    env:
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      # Azure AI configuration
+      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
+      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      # Azure OpenAI configuration
+      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
+      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # A2A configuration
      A2A_AGENT_HOST: http://localhost:5001/
    defaults:
@@ -514,18 +600,19 @@ jobs:
    runs-on: ubuntu-latest
    environment: integration
    env:
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      # Azure AI configuration
+      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
+      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # Azure OpenAI configuration
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
+      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # Azure AI Search (for evaluation samples)
      AZURE_SEARCH_ENDPOINT: ${{ secrets.AZURE_SEARCH_ENDPOINT }}
      AZURE_SEARCH_API_KEY: ${{ secrets.AZURE_SEARCH_API_KEY }}
      AZURE_SEARCH_INDEX_NAME: ${{ secrets.AZURE_SEARCH_INDEX_NAME }}
      # Evaluation sample
-      FOUNDRY_MODEL_WORKFLOW: ${{ vars.FOUNDRY_MODEL_WORKFLOW || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      FOUNDRY_MODEL_EVAL: ${{ vars.FOUNDRY_MODEL_EVAL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_AI_MODEL_DEPLOYMENT_NAME_WORKFLOW: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
    defaults:
      run:
        working-directory: python
@@ -556,11 +643,12 @@ jobs:
    runs-on: ubuntu-latest
    environment: integration
    env:
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      # Azure AI configuration
+      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
+      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # Azure OpenAI configuration
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
      # OpenAI configuration
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
@@ -582,10 +670,10 @@ 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=$AZURE_OPENAI_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
          echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
@@ -606,11 +694,13 @@ jobs:
    runs-on: ubuntu-latest
    environment: integration
    env:
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT || vars.AZURE_AI_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      # Azure AI configuration
+      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
+      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # Azure OpenAI configuration
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
-      AZURE_OPENAI_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_DEPLOYMENT_NAME || vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
+      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # OpenAI configuration
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
@@ -637,10 +727,11 @@ 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=$AZURE_OPENAI_ENDPOINT" >> .env
-          echo "AZURE_OPENAI_DEPLOYMENT_NAME=$AZURE_OPENAI_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=$AZURE_OPENAI_CHAT_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME=$AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME" >> .env
          echo "OPENAI_API_KEY=$OPENAI_API_KEY" >> .env
          echo "OPENAI_CHAT_MODEL_ID=$OPENAI_CHAT_MODEL_ID" >> .env
          echo "OPENAI_RESPONSES_MODEL_ID=$OPENAI_RESPONSES_MODEL_ID" >> .env
@@ -668,12 +759,14 @@ 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
@@ -74,37 +74,6 @@ Contributions must maintain API signature and behavioral compatibility. Contribu
 that include breaking changes will be rejected. Please file an issue to discuss
 your idea or change if you believe that a breaking change is warranted.

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

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

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

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

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

 ### PR - CI Process

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

 # Welcome to Microsoft Agent Framework!

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

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

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

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

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

 ### Python

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

 ### .NET

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

 ## Contributor Resources

@@ -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.
@@ -462,7 +462,7 @@ class FoundryEvals:
 ### Azure AI: FoundryEvals Constants

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

 evaluators = [FoundryEvals.RELEVANCE, FoundryEvals.TOOL_CALL_ACCURACY]
 ```
@@ -31,6 +31,8 @@ The persistence timing and `FunctionResultContent` trimming behaviors are interr

 - **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.
@@ -41,30 +43,33 @@ The persistence timing and `FunctionResultContent` trimming behaviors are interr

 ## Considered Options

- Option 1: Per-run persistence with opt-in FRC (FunctionResultContent) trimming
- Option 2: Opt-in per-service-call persistence (via `RequirePerServiceCallChatHistoryPersistence`)
+- 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: Per-run persistence with opt-in FRC trimming
+### 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 an opt-in behavior to improve consistency with service storage.
+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.
- Bad, because this option alone does not provide a way for users to opt into per-service-call persistence, not satisfying driver E.
+- 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: Opt-in per-service-call persistence (via `RequirePerServiceCallChatHistoryPersistence`)
+### Option 2: Default to per-service-call persistence

-Introduce an optional RequirePerServiceCallChatHistoryPersistence setting to persist chat history after each individual service call within the FIC loop, matching the AI service's behavior. Trailing `FunctionResultContent` trimming is unnecessary with this approach (it is naturally handled).
+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:
- `RequirePerServiceCallChatHistoryPersistence` = `true`
+- `PersistChatHistoryAtEndOfRun` = `false` (default)

- Good, because the stored history matches the service's behavior when opting in for both timing and content, fully satisfying driver A.
+- 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`.
@@ -73,49 +78,39 @@ Settings:

 ## Decision Outcome

-Chosen option: **Option 2: Opt-in per-service-call persistence (via `RequirePerServiceCallChatHistoryPersistence`)**. The existing per-run persistence behavior is retained as-is, requiring no changes from users. Per-service-call persistence is available as an opt-in feature via the `RequirePerServiceCallChatHistoryPersistence` setting. This satisfies drivers B (atomicity) and D (simplicity) for the common case, while fully satisfying driver A (consistency) for users who opt into simulated service-stored behavior. Users who need per-service-call persistence for recoverability (driver C) can enable it explicitly.
+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 `RequirePerServiceCallChatHistoryPersistence`:
+The behavior depends on the combination of `UseProvidedChatClientAsIs` and `PersistChatHistoryAtEndOfRun`:

-| `UseProvidedChatClientAsIs` | `RequirePerServiceCallChatHistoryPersistence` | Behavior |
+| `UseProvidedChatClientAsIs` | `PersistChatHistoryAtEndOfRun` | Behavior |
 |---|---|---|
-| `false` (default) | `false` (default) | **Per-run persistence.** Messages are persisted at the end of the full agent run via the `ChatHistoryProvider`. |
-| `false` | `true` | **Per-service-call persistence (simulated).** A `PerServiceCallChatHistoryPersistingChatClient` middleware is automatically injected into the chat client pipeline between `FunctionInvokingChatClient` and the leaf `IChatClient`. Messages are persisted after each service call. A sentinel `ConversationId` causes FIC to treat the conversation as service-managed. |
-| `true` | `false` | **Per-run persistence.** No middleware is injected because the user has provided a custom chat client stack. Messages are persisted at the end of the run. |
-| `true` | `true` | **User responsibility.** The system checks whether the custom chat client stack includes a `PerServiceCallChatHistoryPersistingChatClient`. If not, a warning is emitted — the user is expected to have added their own per-service-call persistence mechanism. End-of-run persistence is skipped. |
+| `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 per-run persistence is atomic by default — chat history is only updated when the full run succeeds, satisfying driver B.
- Good, because the default mental model is simple: one run = one history update, satisfying driver D.
- Good, because users who opt into `RequirePerServiceCallChatHistoryPersistence` get stored history that matches the service's behavior for both timing and content, fully satisfying driver A.
- Good, because per-service-call persistence preserves intermediate progress if the process is interrupted, satisfying driver C when opted in.
- Good, because no separate `FunctionResultContent` trimming logic is needed when per-service-call persistence is active — it is naturally handled.
- Good, because conflict detection (configurable via `ThrowOnChatHistoryProviderConflict`, `WarnOnChatHistoryProviderConflict`, `ClearOnChatHistoryProviderConflict`) prevents misconfiguration when a service returns a `ConversationId` alongside a configured `ChatHistoryProvider`.
- Bad, because per-service-call persistence (when opted in) may leave chat history in an incomplete state if the run fails mid-loop (e.g., `FunctionCallContent` stored without corresponding `FunctionResultContent`), requiring manual recovery in rare cases.
- Neutral, because users who want per-service-call consistency can opt in via `RequirePerServiceCallChatHistoryPersistence = true`, satisfying driver E.
+- 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

-When `RequirePerServiceCallChatHistoryPersistence` is enabled, the `PerServiceCallChatHistoryPersistingChatClient`
-decorator also updates `session.ConversationId` after each service call. This handles two scenarios:
+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.

-1. **Framework-managed chat history** — the decorator sets a sentinel `ConversationId` on the response
-   so that `FunctionInvokingChatClient` treats the conversation as service-managed (clearing accumulated
-   history between iterations and not injecting duplicate `FunctionCallContent` during approval processing).
-
-2. **Service-stored chat history** — when the service returns a real `ConversationId`, the decorator
-   updates `session.ConversationId` immediately after each service call, rather than deferring the update
-   to the end of the run. This ensures intermediate ConversationId changes are captured even if the
-   process is interrupted mid-loop.
-
-For some service-stored scenarios (e.g., the Conversations API with the Responses API), there is only
-one thread with one ID, so every service call returns the same ConversationId and this per-call update
-makes no practical difference. Enabling `RequirePerServiceCallChatHistoryPersistence` ensures consistent
-per-service-call behavior across all service types regardless of how they manage ConversationIds.
+## 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
@@ -17,7 +17,6 @@

  <PropertyGroup>
    <IsReleaseCandidate>false</IsReleaseCandidate>
-    <IsGenerallyAvailable>false</IsGenerallyAvailable>
  </PropertyGroup>
  
  <PropertyGroup>
@@ -1,4 +1,4 @@
-<Solution>
+<Solution>
  <Configurations>
    <BuildType Name="Debug" />
    <BuildType Name="Publish" />
@@ -105,7 +105,6 @@
  <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_Step02_CodeDefinedSkills/Agent_Step02_CodeDefinedSkills.csproj" />
  </Folder>
  <Folder Name="/Samples/02-agents/AGUI/Step05_StateManagement/">
    <Project Path="samples/02-agents/AGUI/Step05_StateManagement/Client/Client.csproj" />
@@ -122,34 +121,6 @@
    <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" />
@@ -172,6 +143,35 @@
    <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" />
@@ -318,8 +318,8 @@
  <Folder Name="/Samples/05-end-to-end/AspNetAgentAuthorization/">
    <File Path="samples/05-end-to-end/AspNetAgentAuthorization/docker-compose.yml" />
    <File Path="samples/05-end-to-end/AspNetAgentAuthorization/README.md" />
-    <Project Path="samples/05-end-to-end/AspNetAgentAuthorization/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" />
@@ -2,19 +2,17 @@
  <PropertyGroup>
    <!-- Central version prefix - applies to all nuget packages. -->
    <VersionPrefix>1.0.0</VersionPrefix>
-    <RCNumber>5</RCNumber>
+    <RCNumber>4</RCNumber>
    <PackageVersion Condition="'$(IsReleaseCandidate)' == 'true'">$(VersionPrefix)-rc$(RCNumber)</PackageVersion>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260330.1</PackageVersion>
-    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260330.1</PackageVersion>
-    <GitTag>1.0.0-rc5</GitTag>
+    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).260311.1</PackageVersion>
+    <PackageVersion Condition="'$(IsReleaseCandidate)' != 'true' AND '$(VersionSuffix)' == ''">$(VersionPrefix)-preview.260311.1</PackageVersion>
+    <GitTag>1.0.0-rc4</GitTag>

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

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

                        if (approvalRequest.AdditionalProperties != null)
                        {
-                            approvalResponse.AdditionalProperties = [];
+                            approvalResponse.AdditionalProperties = new AdditionalPropertiesDictionary();
                            foreach (var kvp in approvalRequest.AdditionalProperties)
                            {
                                approvalResponse.AdditionalProperties[kvp.Key] = kvp.Value;
@@ -131,9 +131,9 @@ internal sealed class ServerFunctionApprovalClientAgent : DelegatingAIAgent
                    transformedContents ??= CopyContentsUpToIndex(message.Contents, contentIndex);
                    approvalCalls.Remove(functionResult.CallId);
                }
-                else
+                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
        }
@@ -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);
                }
            }
        }
@@ -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;
                    }
@@ -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}");
@@ -1,90 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample demonstrates how to define Agent Skills entirely in code using AgentInlineSkill.
-// No SKILL.md files are needed — skills, resources, and scripts are all defined programmatically.
-//
-// Three approaches are shown using a unit-converter skill:
-// 1. Static resources — inline content provided via AddResource
-// 2. Dynamic resources — computed at runtime via a factory delegate
-// 3. Code scripts — executable delegates the agent can invoke directly
-
-using System.Text.Json;
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using OpenAI.Responses;
-
-// --- Configuration ---
-string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// --- Build the code-defined skill ---
-var unitConverterSkill = new AgentInlineSkill(
-    name: "unit-converter",
-    description: "Convert between common units using a multiplication factor. Use when asked to convert miles, kilometers, pounds, or kilograms.",
-    instructions: """
-        Use this skill when the user asks to convert between units.
-
-        1. Review the conversion-table resource to find the factor for the requested conversion.
-        2. Check the conversion-policy resource for rounding and formatting rules.
-        3. Use the convert script, passing the value and factor from the table.
-        """)
-    // 1. Static Resource: conversion tables
-    .AddResource(
-        "conversion-table",
-        """
-        # Conversion Tables
-
-        Formula: **result = value × factor**
-
-        | From        | To          | Factor   |
-        |-------------|-------------|----------|
-        | miles       | kilometers  | 1.60934  |
-        | kilometers  | miles       | 0.621371 |
-        | pounds      | kilograms   | 0.453592 |
-        | kilograms   | pounds      | 2.20462  |
-        """)
-    // 2. Dynamic Resource: conversion policy (computed at runtime)
-    .AddResource("conversion-policy", () =>
-    {
-        const int Precision = 4;
-        return $"""
-            # Conversion Policy
-
-            **Decimal places:** {Precision}
-            **Format:** Always show both the original and converted values with units
-            **Generated at:** {DateTime.UtcNow:O}
-            """;
-    })
-    // 3. Code Script: convert
-    .AddScript("convert", (double value, double factor) =>
-    {
-        double result = Math.Round(value * factor, 4);
-        return JsonSerializer.Serialize(new { value, factor, result });
-    });
-
-// --- Skills Provider ---
-var skillsProvider = new AgentSkillsProvider(unitConverterSkill);
-
-// --- Agent Setup ---
-AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-    .GetResponsesClient()
-    .AsAIAgent(new ChatClientAgentOptions
-    {
-        Name = "UnitConverterAgent",
-        ChatOptions = new()
-        {
-            Instructions = "You are a helpful assistant that can convert units.",
-        },
-        AIContextProviders = [skillsProvider],
-    },
-    model: deploymentName);
-
-// --- Example: Unit conversion ---
-Console.WriteLine("Converting units with code-defined skills");
-Console.WriteLine(new string('-', 60));
-
-AgentResponse response = await agent.RunAsync(
-    "How many kilometers is a marathon (26.2 miles)? And how many pounds is 75 kilograms?");
-
-Console.WriteLine($"Agent: {response.Text}");
@@ -1,52 +0,0 @@
-# Code-Defined Agent Skills Sample
-
-This sample demonstrates how to define **Agent Skills entirely in code** using `AgentInlineSkill`.
-
-## What it demonstrates
-
- Creating skills programmatically with `AgentInlineSkill` — no SKILL.md files needed
- **Static resources** via `AddResource` with inline content
- **Dynamic resources** via `AddResource` with a factory delegate (computed at runtime)
- **Code scripts** via `AddScript` with a delegate handler
- Using the `AgentSkillsProvider` constructor with inline skills
-
-## Skills Included
-
-### unit-converter (code-defined)
-
-Converts between common units using multiplication factors. Defined entirely in C# code:
-
- `conversion-table` — Static resource with factor table
- `conversion-policy` — Dynamic resource with formatting rules (generated at runtime)
- `convert` — Script that performs `value × factor` conversion
-
-## Running the Sample
-
-### Prerequisites
-
- .NET 10.0 SDK
- Azure OpenAI endpoint with a deployed model
-
-### Setup
-
-```bash
-export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
-export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-### Run
-
-```bash
-dotnet run
-```
-
-### Expected Output
-
-```
-Converting units with code-defined skills
------------------------------------------------------------
-Agent: Here are your conversions:
-
-1. **26.2 miles → 42.16 km** (a marathon distance)
-2. **75 kg → 165.35 lbs**
-```
@@ -1,24 +1,7 @@
 # AgentSkills Samples

-Samples demonstrating Agent Skills capabilities. Each sample shows a different way to define and use skills.
+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_Step02_CodeDefinedSkills](Agent_Step02_CodeDefinedSkills/) | Define skills entirely in C# code using `AgentInlineSkill`, with static/dynamic resources and scripts. |
-
-## Key Concepts
-
-### File-Based vs Code-Defined Skills
-
-| Aspect | File-Based | Code-Defined |
-|--------|-----------|--------------|
-| Definition | `SKILL.md` files on disk | `AgentInlineSkill` instances in C# |
-| Resources | All files in skill directory (filtered by extension) | `AddResource` (static value or delegate-backed) |
-| Scripts | Supported via script executor delegate | `AddScript` delegates |
-| Discovery | Automatic from directory path | Explicit via constructor |
-| Dynamic content | No (static files only) | Yes (factory delegates) |
-| Reusability | Copy skill directory | Inline or shared instances |
-
-For single-source scenarios, use the `AgentSkillsProvider` constructors directly. To combine multiple skill types, use the `AgentSkillsProviderBuilder`.
-
@@ -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);
+}
@@ -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]
    });

@@ -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.
@@ -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);
+    }
+}
@@ -16,7 +16,7 @@ using Qdrant.Client;
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
 var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
 var embeddingDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-3-large";
-var afOverviewUrl = "https://raw.githubusercontent.com/MicrosoftDocs/semantic-kernel-docs/refs/heads/main/agent-framework/overview/index.md";
+var afOverviewUrl = "https://github.com/MicrosoftDocs/semantic-kernel-docs/blob/main/agent-framework/overview/agent-framework-overview.md";
 var afMigrationUrl = "https://raw.githubusercontent.com/MicrosoftDocs/semantic-kernel-docs/refs/heads/main/agent-framework/migration-guide/from-semantic-kernel/index.md";

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

@@ -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.
@@ -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();
-}
@@ -17,10 +17,10 @@ var bingConnectionId = Environment.GetEnvironmentVariable("AZURE_AI_BING_CONNECT
 PersistentAgentsAdministrationClientOptions persistentAgentsClientOptions = new();
 persistentAgentsClientOptions.Retry.NetworkTimeout = TimeSpan.FromMinutes(20);

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

 // Define and configure the Deep Research tool.
@@ -23,14 +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"
@@ -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);
@@ -1,16 +1,15 @@
 // Copyright (c) Microsoft. All rights reserved.

 // This sample demonstrates how the ChatClientAgent persists chat history after each individual
-// call to the AI service, using the RequirePerServiceCallChatHistoryPersistence option.
+// 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 use end-of-run persistence instead (atomic run semantics), remove the
-// RequirePerServiceCallChatHistoryPersistence = true setting (or set it to false). End-of-run
-// persistence is the default behavior.
+// 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.
@@ -54,7 +53,7 @@ static string GetTime([Description("The city name.")] string city) =>
        _ => $"{city}: time data not available."
    };

-// Create the agent — per-service-call persistence is enabled via RequirePerServiceCallChatHistoryPersistence.
+// 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) ?
@@ -64,7 +63,6 @@ AIAgent agent = chatClient.AsAIAgent(
    new ChatClientAgentOptions
    {
        Name = "WeatherAssistant",
-        RequirePerServiceCallChatHistoryPersistence = true,
        ChatOptions = new()
        {
            Instructions = "You are a helpful assistant. When asked about multiple cities, call the appropriate tool for each city.",
@@ -1,19 +1,16 @@
 # In-Function-Loop Checkpointing

-This sample demonstrates how `ChatClientAgent` can persist chat history after each individual call to the AI service using the `RequirePerServiceCallChatHistoryPersistence` option. This per-service-call persistence ensures intermediate progress is saved during the function invocation loop.
+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 enabling `RequirePerServiceCallChatHistoryPersistence = true`, chat history is persisted after each service call via the `PerServiceCallChatHistoryPersistingChatClient` decorator:
+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 `PerServiceCallChatHistoryPersistingChatClient` decorator is inserted into the chat client pipeline
- Before each service call, the decorator loads history from the `ChatHistoryProvider` and prepends it to the request
+- 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

-By default (without `RequirePerServiceCallChatHistoryPersistence`), chat history is persisted at the end of the full agent run instead. To use per-service-call persistence, set `RequirePerServiceCallChatHistoryPersistence = true` on `ChatClientAgentOptions`.
-
-With `RequirePerServiceCallChatHistoryPersistence` = true, the behavior matches that of chat history stored in the underlying AI service exactly.
+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
@@ -29,7 +26,7 @@ The sample asks the agent about the weather and time in three cities. The model
 ```
 ChatClientAgent
  └─ FunctionInvokingChatClient    (handles tool call loop)
-       └─ PerServiceCallChatHistoryPersistingChatClient  (persists after each service call)
+       └─ ChatHistoryPersistingChatClient  (persists after each service call)
            └─ Leaf IChatClient            (Azure OpenAI)
 ```

@@ -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);
@@ -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
-```
@@ -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>
@@ -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."));
@@ -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.
@@ -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));
@@ -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
-```
@@ -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>
@@ -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();
@@ -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
-```
@@ -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>
@@ -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);
-}
@@ -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"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-Navigate to the AgentsWithFoundry sample directory and run:
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step03_UsingFunctionTools
-```
@@ -1,15 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,30 +0,0 @@
-# Using Function Tools with Approvals via the Responses API
-
-This sample demonstrates how to use function tools that require human-in-the-loop approval before execution.
-
-## What this sample demonstrates
-
- Creating function tools that require approval using `ApprovalRequiredAIFunction`
- Handling approval requests from the agent
- Passing approval responses back to the agent
- No server-side agent creation or cleanup required
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step04_UsingFunctionToolsWithApprovals
-```
@@ -1,15 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,29 +0,0 @@
-# Structured Output with the Responses API
-
-This sample demonstrates how to configure an agent to produce structured output using JSON schema.
-
-## What this sample demonstrates
-
- Using `RunAsync<T>()` to get typed structured output from the agent
- Deserializing streamed responses into structured types
- No server-side agent creation or cleanup required
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step05_StructuredOutput
-```
@@ -1,15 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,30 +0,0 @@
-# Persisted Conversations with the Responses API
-
-This sample demonstrates how to persist and resume agent conversations using session serialization.
-
-## What this sample demonstrates
-
- Serializing agent sessions to JSON for persistence
- Saving and loading sessions from disk
- Resuming conversations with preserved context
- No server-side agent creation or cleanup required
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step06_PersistedConversations
-```
@@ -1,31 +0,0 @@
-# Observability with the Responses API
-
-This sample demonstrates how to add OpenTelemetry observability to an agent using console and Azure Monitor exporters.
-
-## What this sample demonstrates
-
- Configuring OpenTelemetry tracing with console exporter
- Optional Azure Application Insights integration
- Using `.AsBuilder().UseOpenTelemetry()` to add telemetry to the agent
- No server-side agent creation or cleanup required
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$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
-```
@@ -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;
-        }
-    }
-}
@@ -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
-```
@@ -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)}");
@@ -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
-```
@@ -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>
@@ -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
-```
@@ -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>
@@ -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
-```
@@ -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
-```
@@ -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 $"The weather in {location} is {this._weatherSummary}.";
-        }
-    }
-
-    internal sealed class CurrentTimeProvider
-    {
-        private readonly TimeProvider _timeProvider = TimeProvider.System;
-
-        /// <summary>
-        /// Provides the current date and time.
-        /// </summary>
-        /// <remarks>
-        /// This class returns the current date and time using the system's clock.
-        /// </remarks>
-        /// <summary>
-        /// Gets the current date and time.
-        /// </summary>
-        /// <param name="location">The location to get the current time for (not used in this implementation).</param>
-        /// <returns>The current date and time as a <see cref="DateTimeOffset"/>.</returns>
-        public DateTimeOffset GetCurrentTime(string location)
-        {
-            return this._timeProvider.GetLocalNow();
-        }
-    }
-}
@@ -1,29 +0,0 @@
-# Using Plugins with the Responses API
-
-This sample shows how to use plugins with a `ChatClientAgent` using the Responses API directly, with dependency injection for plugin services.
-
-## What this sample demonstrates
-
- Creating plugin classes with injected dependencies
- Registering services and building a service provider
- Passing `services` to the `ChatClientAgent` via the options-based constructor
- Using `AIFunctionFactory` to expose plugin methods as AI tools
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,28 +0,0 @@
-# Code Interpreter with the Responses API
-
-This sample shows how to use the Code Interpreter tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Using `HostedCodeInterpreterTool` with `ChatClientAgent`
- Extracting code input and output from agent responses
- Handling code interpreter annotations and file citations
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,29 +0,0 @@
-# Computer Use with the Responses API
-
-This sample shows how to use the Computer Use tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Using `FoundryAITool.CreateComputerTool()` with `ChatClientAgent`
- Processing computer call actions (click, type, key press)
- Managing the computer use interaction loop with screenshots
- Handling the Azure Agents API workaround for `previous_response_id` with `computer_call_output`
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="computer-use-preview"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,29 +0,0 @@
-# File Search with the Responses API
-
-This sample shows how to use the File Search tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Uploading files and creating vector stores via `AIProjectClient`
- Using `HostedFileSearchTool` with `ChatClientAgent`
- Handling file citation annotations in agent responses
- Cleaning up file resources after use
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,20 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,29 +0,0 @@
-# OpenAPI Tools with the Responses API
-
-This sample shows how to use OpenAPI tools with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Defining an OpenAPI specification inline
- Creating an `OpenAPIFunctionDefinition` for the REST Countries API
- Using `FoundryAITool.CreateOpenApiTool()` with `ChatClientAgent`
- Server-side execution of OpenAPI tool calls
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,36 +0,0 @@
-# Bing Custom Search with the Responses API
-
-This sample shows how to use the Bing Custom Search tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Configuring `BingCustomSearchToolParameters` with connection ID and instance name
- Using `FoundryAITool.CreateBingCustomSearchTool()` with `ChatClientAgent`
- Processing search results from agent responses
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
- Bing Custom Search resource configured with a connection ID
-
-Set the following environment variables:
-
-```powershell
-$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
-```
@@ -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>
@@ -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
-```
@@ -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>
@@ -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($"Created agent: {agent.Name}");
-
-// Run the agent with a sample query
-AgentResponse response = await agent.RunAsync("What data is available in the connected Fabric workspace?");
-
-Console.WriteLine("\n=== Agent Response ===");
-foreach (var message in response.Messages)
-{
-    Console.WriteLine(message.Text);
-}
@@ -1,30 +0,0 @@
-# Microsoft Fabric with the Responses API
-
-This sample shows how to use the Microsoft Fabric tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Configuring `FabricDataAgentToolOptions` with project connections
- Using `FoundryAITool.CreateMicrosoftFabricTool()` with `ChatClientAgent`
- Querying data available through a Fabric connection
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
- Microsoft Fabric connection configured in your Microsoft Foundry project
-
-Set the following environment variables:
-
-```powershell
-$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
-```
@@ -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>
@@ -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}}
-            """);
-    }
-}
@@ -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
-```
@@ -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>
@@ -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"
-$env:AZURE_AI_MEMORY_STORE_ID="your-memory-store-name"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,29 +0,0 @@
-# Local MCP with the Responses API
-
-This sample demonstrates how to use a local MCP (Model Context Protocol) client with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Connecting to an MCP server via HTTP (Streamable HTTP transport)
- Resolving MCP tools locally and wrapping them with logging
- Using `DelegatingAIFunction` to add custom behavior to MCP tools
- Passing locally-resolved MCP tools to `ChatClientAgent`
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,81 +0,0 @@
-# Getting started with Foundry Agents
-
-These samples demonstrate how to use Azure AI Foundry with Agent Framework.
-
-## Quick start
-
-The simplest way to create a Foundry agent is using the `FoundryAgent` type directly:
-
-```csharp
-FoundryAgent agent = new(
-    new Uri(endpoint),
-    new AzureCliCredential(),
-    model: "gpt-4o-mini",
-    instructions: "You are good at telling jokes.",
-    name: "JokerAgent");
-
-Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
-```
-
-Or using the `AIProjectClient.AsAIAgent(...)` extensions:
-
-```csharp
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
-
-FoundryAgent agent = aiProjectClient.AsAIAgent(
-    model: deploymentName,
-    instructions: "You are good at telling jokes.",
-    name: "JokerAgent");
-```
-
-## Prerequisites
-
- .NET 10 SDK or later
- Foundry project endpoint
- Azure CLI installed and authenticated
-
-Set:
-
-```powershell
-$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
-```
@@ -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>
@@ -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));
@@ -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
@@ -6,16 +6,20 @@

    <Nullable>enable</Nullable>
    <ImplicitUsings>enable</ImplicitUsings>
-    <NoWarn>$(NoWarn);MAAI001</NoWarn>
  </PropertyGroup>

  <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>
@@ -0,0 +1,292 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use Microsoft.Extensions.AI.Evaluation.Quality to evaluate
+// an Agent Framework agent's response quality with a self-reflection loop.
+//
+// It uses GroundednessEvaluator, RelevanceEvaluator, and CoherenceEvaluator to score responses,
+// then iteratively asks the agent to improve based on evaluation feedback.
+//
+// Based on: Reflexion: Language Agents with Verbal Reinforcement Learning (NeurIPS 2023)
+// Reference: https://arxiv.org/abs/2303.11366
+//
+// For more details, see:
+// https://learn.microsoft.com/dotnet/ai/evaluation/libraries
+
+using Azure.AI.OpenAI;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.AI.Evaluation;
+using Microsoft.Extensions.AI.Evaluation.Quality;
+using Microsoft.Extensions.AI.Evaluation.Safety;
+
+using ChatMessage = Microsoft.Extensions.AI.ChatMessage;
+using ChatRole = Microsoft.Extensions.AI.ChatRole;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+string openAiEndpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string evaluatorDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? deploymentName;
+
+Console.WriteLine("=" + new string('=', 79));
+Console.WriteLine("SELF-REFLECTION EVALUATION SAMPLE");
+Console.WriteLine("=" + new string('=', 79));
+Console.WriteLine();
+
+// Initialize Azure credentials and client
+// 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);
+
+// Set up the LLM-based chat client for quality evaluators
+IChatClient chatClient = new AzureOpenAIClient(new Uri(openAiEndpoint), credential)
+    .GetChatClient(evaluatorDeploymentName)
+    .AsIChatClient();
+
+// Configure evaluation: quality evaluators use the LLM, safety evaluators use Azure AI Foundry
+ContentSafetyServiceConfiguration safetyConfig = new(
+    credential: credential,
+    endpoint: new Uri(endpoint));
+
+ChatConfiguration chatConfiguration = safetyConfig.ToChatConfiguration(
+    originalChatConfiguration: new ChatConfiguration(chatClient));
+
+// Create a test agent
+AIAgent agent = await aiProjectClient.CreateAIAgentAsync(
+    name: "KnowledgeAgent",
+    model: deploymentName,
+    instructions: "You are a helpful assistant. Answer questions accurately based on the provided context.");
+Console.WriteLine($"Created agent: {agent.Name}");
+Console.WriteLine();
+
+// Example question and grounding context
+const string Question = """
+    What are the main benefits of using Azure AI Foundry for building AI applications?
+    """;
+
+const string Context = """
+    Azure AI Foundry is a comprehensive platform for building, deploying, and managing AI applications.
+    Key benefits include:
+    1. Unified development environment with support for multiple AI frameworks and models
+    2. Built-in safety and security features including content filtering and red teaming tools
+    3. Scalable infrastructure that handles deployment and monitoring automatically
+    4. Integration with Azure services like Azure OpenAI, Cognitive Services, and Machine Learning
+    5. Evaluation tools for assessing model quality, safety, and performance
+    6. Support for RAG (Retrieval-Augmented Generation) patterns with vector search
+    7. Enterprise-grade compliance and governance features
+    """;
+
+Console.WriteLine("Question:");
+Console.WriteLine(Question);
+Console.WriteLine();
+
+// Run evaluations
+try
+{
+    await RunSelfReflectionWithGroundedness(agent, Question, Context, chatConfiguration);
+    await RunQualityEvaluation(agent, Question, Context, chatConfiguration);
+    await RunCombinedQualityAndSafetyEvaluation(agent, Question, chatConfiguration);
+}
+finally
+{
+    // Cleanup
+    await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
+    Console.WriteLine();
+    Console.WriteLine("Cleanup: Agent deleted.");
+}
+
+// ============================================================================
+// Implementation Functions
+// ============================================================================
+
+static async Task RunSelfReflectionWithGroundedness(
+    AIAgent agent, string question, string context, ChatConfiguration chatConfiguration)
+{
+    Console.WriteLine("Running Self-Reflection with Groundedness Evaluation...");
+    Console.WriteLine();
+
+    GroundednessEvaluator groundednessEvaluator = new();
+    GroundednessEvaluatorContext groundingContext = new(context);
+
+    const int MaxReflections = 3;
+    double bestScore = 0;
+
+    string currentPrompt = $"Context: {context}\n\nQuestion: {question}";
+
+    for (int i = 0; i < MaxReflections; i++)
+    {
+        Console.WriteLine($"Iteration {i + 1}/{MaxReflections}:");
+        Console.WriteLine(new string('-', 40));
+
+        // Create a new session for each reflection iteration so that
+        // conversation context does not carry over between runs. This keeps
+        // each evaluation independent and avoids biasing groundedness scores.
+        AgentSession session = await agent.CreateSessionAsync();
+        AgentResponse agentResponse = await agent.RunAsync(currentPrompt, session);
+        string responseText = agentResponse.Text;
+
+        Console.WriteLine($"Response: {responseText[..Math.Min(150, responseText.Length)]}...");
+
+        List<ChatMessage> messages =
+        [
+            new(ChatRole.User, currentPrompt),
+        ];
+        ChatResponse chatResponse = new(new ChatMessage(ChatRole.Assistant, responseText));
+
+        EvaluationResult result = await groundednessEvaluator.EvaluateAsync(
+            messages,
+            chatResponse,
+            chatConfiguration,
+            additionalContext: [groundingContext]);
+
+        NumericMetric groundedness = result.Get<NumericMetric>(GroundednessEvaluator.GroundednessMetricName);
+        double score = groundedness.Value ?? 0;
+        string rating = groundedness.Interpretation?.Rating.ToString() ?? "N/A";
+
+        Console.WriteLine($"Groundedness score: {score:F1}/5 (Rating: {rating})");
+        Console.WriteLine();
+
+        if (score > bestScore)
+        {
+            bestScore = score;
+        }
+
+        if (score >= 4.0 || i == MaxReflections - 1)
+        {
+            if (score >= 4.0)
+            {
+                Console.WriteLine("Good groundedness achieved!");
+            }
+
+            break;
+        }
+
+        // Ask for improvement in the next iteration, including the previous response
+        // so the LLM knows what to improve on (each iteration uses a new session).
+        currentPrompt = $"""
+            Context: {context}
+
+            Your previous answer scored {score}/5 on groundedness.
+            Your previous answer was:
+            {responseText}
+
+            Please improve your answer to be more grounded in the provided context.
+            Only include information that is directly supported by the context.
+
+            Question: {question}
+            """;
+        Console.WriteLine("Requesting improvement...");
+        Console.WriteLine();
+    }
+
+    Console.WriteLine($"Best groundedness score: {bestScore:F1}/5");
+    Console.WriteLine(new string('=', 80));
+    Console.WriteLine();
+}
+
+static async Task RunQualityEvaluation(
+    AIAgent agent, string question, string context, ChatConfiguration chatConfiguration)
+{
+    Console.WriteLine("Running Quality Evaluation (Relevance, Coherence, Groundedness)...");
+    Console.WriteLine();
+
+    IEvaluator[] evaluators =
+    [
+        new RelevanceEvaluator(),
+        new CoherenceEvaluator(),
+        new GroundednessEvaluator(),
+    ];
+
+    CompositeEvaluator compositeEvaluator = new(evaluators);
+    GroundednessEvaluatorContext groundingContext = new(context);
+
+    string prompt = $"Context: {context}\n\nQuestion: {question}";
+
+    AgentSession session = await agent.CreateSessionAsync();
+    AgentResponse agentResponse = await agent.RunAsync(prompt, session);
+    string responseText = agentResponse.Text;
+
+    Console.WriteLine($"Response: {responseText[..Math.Min(150, responseText.Length)]}...");
+    Console.WriteLine();
+
+    List<ChatMessage> messages =
+    [
+        new(ChatRole.User, prompt),
+    ];
+    ChatResponse chatResponse = new(new ChatMessage(ChatRole.Assistant, responseText));
+
+    EvaluationResult result = await compositeEvaluator.EvaluateAsync(
+        messages,
+        chatResponse,
+        chatConfiguration,
+        additionalContext: [groundingContext]);
+
+    foreach (EvaluationMetric metric in result.Metrics.Values)
+    {
+        if (metric is NumericMetric n)
+        {
+            string rating = n.Interpretation?.Rating.ToString() ?? "N/A";
+            Console.WriteLine($"  {n.Name,-20} Score: {n.Value:F1}/5  Rating: {rating}");
+        }
+    }
+
+    Console.WriteLine(new string('=', 80));
+    Console.WriteLine();
+}
+
+static async Task RunCombinedQualityAndSafetyEvaluation(
+    AIAgent agent, string question, ChatConfiguration chatConfiguration)
+{
+    Console.WriteLine("Running Combined Quality + Safety Evaluation...");
+    Console.WriteLine();
+
+    IEvaluator[] evaluators =
+    [
+        new RelevanceEvaluator(),
+        new CoherenceEvaluator(),
+        new ContentHarmEvaluator(),
+        new ProtectedMaterialEvaluator(),
+    ];
+
+    CompositeEvaluator compositeEvaluator = new(evaluators);
+
+    AgentSession session = await agent.CreateSessionAsync();
+    AgentResponse agentResponse = await agent.RunAsync(question, session);
+    string responseText = agentResponse.Text;
+
+    Console.WriteLine($"Response: {responseText[..Math.Min(150, responseText.Length)]}...");
+    Console.WriteLine();
+
+    List<ChatMessage> messages =
+    [
+        new(ChatRole.User, question), // No context in this evaluation — testing quality and safety on raw question
+    ];
+    ChatResponse chatResponse = new(new ChatMessage(ChatRole.Assistant, responseText));
+
+    EvaluationResult result = await compositeEvaluator.EvaluateAsync(
+        messages,
+        chatResponse,
+        chatConfiguration);
+
+    Console.WriteLine("Quality Metrics:");
+    foreach (EvaluationMetric metric in result.Metrics.Values)
+    {
+        if (metric is NumericMetric n)
+        {
+            string rating = n.Interpretation?.Rating.ToString() ?? "N/A";
+            bool failed = n.Interpretation?.Failed ?? false;
+            Console.WriteLine($"  {n.Name,-25} Score: {n.Value:F1,-6} Rating: {rating,-15} Failed: {failed}");
+        }
+        else if (metric is BooleanMetric b)
+        {
+            string rating = b.Interpretation?.Rating.ToString() ?? "N/A";
+            bool failed = b.Interpretation?.Failed ?? false;
+            Console.WriteLine($"  {b.Name,-25} Value: {b.Value,-6} Rating: {rating,-15} Failed: {failed}");
+        }
+    }
+
+    Console.WriteLine(new string('=', 80));
+}
@@ -0,0 +1,118 @@
+# Self-Reflection Evaluation with Groundedness Assessment
+
+This sample demonstrates the self-reflection pattern using Agent Framework with `Microsoft.Extensions.AI.Evaluation.Quality` evaluators. The agent iteratively improves its responses based on real groundedness evaluation scores.
+
+For details on the self-reflection approach, see [Reflexion: Language Agents with Verbal Reinforcement Learning](https://arxiv.org/abs/2303.11366) (NeurIPS 2023).
+
+## What this sample demonstrates
+
+- Self-reflection loop that improves responses using real `GroundednessEvaluator` scores
+- Using `RelevanceEvaluator` and `CoherenceEvaluator` for multi-metric quality assessment
+- Combining quality and safety evaluators with `CompositeEvaluator`
+- Configuring `ContentSafetyServiceConfiguration` for safety evaluators alongside LLM-based quality evaluators
+- Tracking improvement across iterations
+
+## 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)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+### Azure Resources Required
+
+1. **Azure AI Hub and Project**: Create these in the Azure Portal
+   - Follow: https://learn.microsoft.com/azure/ai-foundry/how-to/create-projects
+2. **Azure OpenAI Deployment**: Deploy a model (e.g., gpt-4o or gpt-4o-mini)
+   - Agent model: Used to generate responses
+   - Evaluator model: Quality evaluators use an LLM; best results with GPT-4o
+3. **Azure CLI**: Install and authenticate with `az login`
+
+### Environment Variables
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-project.api.azureml.ms"  # Azure Foundry project endpoint
+$env:AZURE_OPENAI_ENDPOINT="https://your-openai.openai.azure.com/"         # Azure OpenAI endpoint (for quality evaluators)
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"                   # Model deployment name
+```
+
+**Note**: For best evaluation results, use GPT-4o or GPT-4o-mini as the evaluator model. The groundedness evaluator has been tested and tuned for these models.
+
+## Run the sample
+
+Navigate to the sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step02_SelfReflection
+dotnet run
+```
+
+## Expected behavior
+
+The sample runs three evaluation scenarios:
+
+### 1. Self-Reflection with Groundedness
+- Asks a question with grounding context
+- Evaluates response groundedness using `GroundednessEvaluator`
+- If score is below 4/5, asks the agent to improve with feedback
+- Repeats up to 3 iterations
+- Tracks and reports the best score achieved
+
+### 2. Quality Evaluation
+- Evaluates a single response with multiple quality evaluators:
+  - `RelevanceEvaluator` — is the response relevant to the question?
+  - `CoherenceEvaluator` — is the response logically coherent?
+  - `GroundednessEvaluator` — is the response grounded in the provided context?
+
+### 3. Combined Quality + Safety Evaluation
+- Runs both quality and safety evaluators together:
+  - `RelevanceEvaluator`, `CoherenceEvaluator` (quality)
+  - `ContentHarmEvaluator` (safety — violence, hate, sexual, self-harm)
+  - `ProtectedMaterialEvaluator` (safety — copyrighted content detection)
+
+## Understanding the Evaluation
+
+### Groundedness Score (1-5 scale)
+
+The `GroundednessEvaluator` measures how well the agent's response is grounded in the provided context:
+
+- **5** = Excellent - Response is fully grounded in context
+- **4** = Good - Mostly grounded with minor deviations
+- **3** = Fair - Partially grounded but includes unsupported claims
+- **2** = Poor - Significant amount of ungrounded content
+- **1** = Very Poor - Response is largely unsupported by context
+
+### Self-Reflection Process
+
+1. **Initial Response**: Agent generates answer based on question + context
+2. **Evaluation**: `GroundednessEvaluator` scores the response (1-5)
+3. **Feedback**: If score < 4, agent receives the score and is asked to improve
+4. **Iteration**: Process repeats until good score or max iterations
+
+## Best Practices
+
+1. **Provide Complete Context**: Ensure grounding context contains all information needed to answer the question
+2. **Clear Instructions**: Give the agent clear instructions about staying grounded in context
+3. **Use Quality Models**: GPT-4o recommended for evaluation tasks
+4. **Multiple Evaluators**: Use combination of evaluators (groundedness + relevance + coherence)
+5. **Batch Processing**: For production, process multiple questions in batch
+
+## Related Resources
+
+- [Reflexion Paper (NeurIPS 2023)](https://arxiv.org/abs/2303.11366)
+- [Microsoft.Extensions.AI.Evaluation Libraries](https://learn.microsoft.com/dotnet/ai/evaluation/libraries)
+- [GroundednessEvaluator API Reference](https://learn.microsoft.com/dotnet/api/microsoft.extensions.ai.evaluation.quality.groundednessevaluator)
+- [Azure AI Foundry Evaluation Service](https://learn.microsoft.com/azure/ai-foundry/how-to/develop/evaluate-sdk)
+
+## Next Steps
+
+After running self-reflection evaluation:
+1. Implement similar patterns for other quality metrics (relevance, coherence, fluency)
+2. Integrate into CI/CD pipeline for continuous quality assurance
+3. Explore the Safety Evaluation sample (FoundryAgents_Evaluations_Step01_RedTeaming) for content safety assessment
@@ -0,0 +1,21 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <NoWarn>$(NoWarn);IDE0059</NoWarn>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.Projects" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,50 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create and use AI agents with Azure Foundry Agents as the backend.
+
+using Azure.AI.Projects;
+using Azure.AI.Projects.Agents;
+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";
+
+const string JokerName = "JokerAgent";
+
+// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Define the agent you want to create. (Prompt Agent in this case)
+AgentVersionCreationOptions options = new(new PromptAgentDefinition(model: deploymentName) { Instructions = "You are good at telling jokes." });
+
+// Azure.AI.Agents SDK creates and manages agent by name and versions.
+// You can create a server side agent version with the Azure.AI.Agents SDK client below.
+AgentVersion createdAgentVersion = aiProjectClient.Agents.CreateAgentVersion(agentName: JokerName, options);
+
+// Note:
+//      agentVersion.Id = "<agentName>:<versionNumber>",
+//      agentVersion.Version = <versionNumber>,
+//      agentVersion.Name = <agentName>
+
+// You can use an AIAgent with an already created server side agent version.
+AIAgent existingJokerAgent = aiProjectClient.AsAIAgent(createdAgentVersion);
+
+// You can also create another AIAgent version by providing the same name with a different definition/instruction.
+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 by just providing its name.
+AIAgent jokerAgentLatest = await aiProjectClient.GetAIAgentAsync(name: JokerName);
+AgentVersion latestAgentVersion = jokerAgentLatest.GetService<AgentVersion>()!;
+
+// The AIAgent version can be accessed via the GetService method.
+Console.WriteLine($"Latest agent version id: {latestAgentVersion.Id}");
+
+// Once you have the AIAgent, you can invoke it like any other AIAgent.
+Console.WriteLine(await jokerAgentLatest.RunAsync("Tell me a joke about a pirate."));
+
+// Cleanup by agent name removes both agent versions created.
+await aiProjectClient.Agents.DeleteAgentAsync(existingJokerAgent.Name);
@@ -0,0 +1,40 @@
+# Creating and Managing AI Agents with Versioning
+
+This sample demonstrates how to create and manage AI agents with Azure Foundry Agents, including:
+- Creating agents with different versions
+- Retrieving agents by version or latest version
+- Running multi-turn conversations with agents
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+```
+
+## Run the sample
+
+Navigate to the FoundryAgents sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents
+dotnet run --project .\FoundryAgents_Step01.1_Basics
+```
+
+## What this sample demonstrates
+
+1. **Creating agents with versions**: Shows how to create multiple versions of the same agent with different instructions
+2. **Retrieving agents**: Demonstrates retrieving agents by specific version or getting the latest version
+3. **Multi-turn conversations**: Shows how to use threads to maintain conversation context across multiple agent runs
+4. **Agent cleanup**: Demonstrates proper resource cleanup by deleting agents
@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">

  <PropertyGroup>
    <OutputType>Exe</OutputType>
@@ -0,0 +1,39 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create and use a simple AI agent with Azure Foundry Agents as the backend.
+
+using Azure.AI.Projects;
+using Azure.AI.Projects.Agents;
+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";
+
+const string JokerInstructions = "You are good at telling jokes.";
+const string JokerName = "JokerAgent";
+
+// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Define the agent you want to create. (Prompt Agent in this case)
+AgentVersionCreationOptions options = new(new PromptAgentDefinition(model: deploymentName) { Instructions = JokerInstructions });
+
+// Azure.AI.Agents SDK creates and manages agent by name and versions.
+// You can create a server side agent version with the Azure.AI.Agents SDK client below.
+AgentVersion agentVersion = aiProjectClient.Agents.CreateAgentVersion(agentName: JokerName, options);
+
+// You can use an AIAgent with an already created server side agent version.
+AIAgent jokerAgent = aiProjectClient.AsAIAgent(agentVersion);
+
+// Invoke the agent with streaming support.
+await foreach (AgentResponseUpdate update in jokerAgent.RunStreamingAsync("Tell me a joke about a pirate."))
+{
+    Console.WriteLine(update);
+}
+
+// Cleanup by agent name removes the agent version created.
+await aiProjectClient.Agents.DeleteAgentAsync(jokerAgent.Name);
@@ -0,0 +1,46 @@
+# Running a Simple AI Agent with Streaming
+
+This sample demonstrates how to create and run a simple AI agent with Azure Foundry Agents, including both text and streaming responses.
+
+## What this sample demonstrates
+
+- Creating a simple AI agent with instructions
+- Running an agent with text output
+- Running an agent with streaming output
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure 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 Azure 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
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+```
+
+## Run the sample
+
+Navigate to the FoundryAgents sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents
+dotnet run --project .\FoundryAgents_Step01.2_Running
+```
+
+## Expected behavior
+
+The sample will:
+
+1. Create an agent named "JokerAgent" with instructions to tell jokes
+2. Run the agent with a text prompt and display the response
+3. Run the agent again with streaming to display the response as it's generated
+4. Clean up resources by deleting the agent
+
@@ -9,6 +9,7 @@
  </PropertyGroup>

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

@@ -0,0 +1,56 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create and use a simple AI agent with a multi-turn conversation.
+
+using Azure.AI.Extensions.OpenAI;
+using Azure.AI.Projects;
+using Azure.AI.Projects.Agents;
+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";
+
+const string JokerInstructions = "You are good at telling jokes.";
+const string JokerName = "JokerAgent";
+
+// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Define the agent you want to create. (Prompt Agent in this case)
+AgentVersionCreationOptions options = new(new PromptAgentDefinition(model: deploymentName) { Instructions = JokerInstructions });
+
+// Retrieve an AIAgent for the created server side agent version.
+ChatClientAgent jokerAgent = await aiProjectClient.CreateAIAgentAsync(name: JokerName, options);
+
+// Invoke the agent with a multi-turn conversation, where the context is preserved in the session object.
+// Create a conversation in the server
+ProjectConversationsClient conversationsClient = aiProjectClient.GetProjectOpenAIClient().GetProjectConversationsClient();
+ProjectConversation conversation = await conversationsClient.CreateProjectConversationAsync();
+
+// Providing the conversation Id is not strictly necessary, but by not providing it no information will show up in the Foundry Project UI as conversations.
+// Sessions that don't have a conversation Id will work based on the `PreviousResponseId`.
+AgentSession session = await jokerAgent.CreateSessionAsync(conversation.Id);
+
+Console.WriteLine(await jokerAgent.RunAsync("Tell me a joke about a pirate.", session));
+Console.WriteLine(await jokerAgent.RunAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", session));
+
+// Invoke the agent with a multi-turn conversation and streaming, where the context is preserved in the session object.
+session = await jokerAgent.CreateSessionAsync(conversation.Id);
+await foreach (AgentResponseUpdate update in jokerAgent.RunStreamingAsync("Tell me a joke about a pirate.", session))
+{
+    Console.WriteLine(update);
+}
+await foreach (AgentResponseUpdate update in jokerAgent.RunStreamingAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", session))
+{
+    Console.WriteLine(update);
+}
+
+// Cleanup by agent name removes the agent version created.
+await aiProjectClient.Agents.DeleteAgentAsync(jokerAgent.Name);
+
+// Cleanup the conversation created.
+await conversationsClient.DeleteConversationAsync(conversation.Id);
@@ -0,0 +1,59 @@
+# Multi-turn Conversation with AI Agents
+
+This sample demonstrates how to implement multi-turn conversations with AI agents, where context is preserved across multiple agent runs using threads and conversation IDs.
+
+## What this sample demonstrates
+
+- Creating an AI agent with instructions
+- Creating a project conversation to track conversations in the Foundry UI
+- Using threads with conversation IDs to maintain conversation context
+- Running multi-turn conversations with text output
+- Running multi-turn conversations with streaming output
+- Managing agent and conversation lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure 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 Azure 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
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+```
+
+## Run the sample
+
+Navigate to the FoundryAgents sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents
+dotnet run --project .\FoundryAgents_Step02_MultiturnConversation
+```
+
+## Expected behavior
+
+The sample will:
+
+1. Create an agent named "JokerAgent" with instructions to tell jokes
+2. Create a project conversation to enable visibility in the Azure Foundry UI
+3. Create a thread linked to the conversation ID for context tracking
+4. Run the agent with a text prompt and display the response
+5. Send a follow-up message to the same thread, demonstrating context preservation
+6. Create a new thread sharing the same conversation ID and run the agent with streaming
+7. Send a follow-up streaming message to demonstrate multi-turn streaming
+8. Clean up resources by deleting the agent and conversation
+
+## Conversation ID vs PreviousResponseId
+
+When working with multi-turn conversations, there are two approaches:
+
+- **With Conversation ID**: By passing a `conversation.Id` to `CreateSessionAsync()`, the conversation will be visible in the Azure Foundry Project UI. This is useful for tracking and debugging conversations.
+- **Without Conversation ID**: Sessions created without a conversation ID still work correctly, maintaining context via `PreviousResponseId`. However, these conversations may not appear in the Foundry UI.
+
@@ -9,6 +9,7 @@
  </PropertyGroup>

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

@@ -0,0 +1,54 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use an agent with function tools.
+// It shows both non-streaming and streaming agent interactions using weather-related tools.
+
+using System.ComponentModel;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+[Description("Get the weather for a given location.")]
+static string GetWeather([Description("The location to get the weather for.")] string location)
+    => $"The weather in {location} is cloudy with a high of 15°C.";
+
+const string AssistantInstructions = "You are a helpful assistant that can get weather information.";
+const string AssistantName = "WeatherAssistant";
+
+// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Define the agent with function tools.
+AITool tool = AIFunctionFactory.Create(GetWeather);
+
+// Create AIAgent directly
+var newAgent = await aiProjectClient.CreateAIAgentAsync(name: AssistantName, model: deploymentName, instructions: AssistantInstructions, tools: [tool]);
+
+// Getting an already existing agent by name with tools.
+/* 
+ * IMPORTANT: Since agents that are stored in the server only know the definition of the function tools (JSON Schema),
+ * you need to provided all invocable function tools when retrieving the agent so it can invoke them automatically.
+ * If no invocable tools are provided, the function calling needs to handled manually.
+ */
+var existingAgent = await aiProjectClient.GetAIAgentAsync(name: AssistantName, tools: [tool]);
+
+// Non-streaming agent interaction with function tools.
+AgentSession session = await existingAgent.CreateSessionAsync();
+Console.WriteLine(await existingAgent.RunAsync("What is the weather like in Amsterdam?", session));
+
+// Streaming agent interaction with function tools.
+session = await existingAgent.CreateSessionAsync();
+await foreach (AgentResponseUpdate update in existingAgent.RunStreamingAsync("What is the weather like in Amsterdam?", session))
+{
+    Console.WriteLine(update);
+}
+
+// Cleanup by agent name removes the agent version created.
+await aiProjectClient.Agents.DeleteAgentAsync(existingAgent.Name);
@@ -0,0 +1,48 @@
+# Using Function Tools with AI Agents
+
+This sample demonstrates how to use function tools with AI agents, allowing agents to call custom functions to retrieve information.
+
+## What this sample demonstrates
+
+- Creating function tools using AIFunctionFactory
+- Passing function tools to an AI agent
+- Running agents with function tools (text output)
+- Running agents with function tools (streaming output)
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure 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 Azure 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" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+```
+
+## Run the sample
+
+Navigate to the FoundryAgents sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents
+dotnet run --project .\FoundryAgents_Step03.1_UsingFunctionTools
+```
+
+## Expected behavior
+
+The sample will:
+
+1. Create an agent named "WeatherAssistant" with a GetWeather function tool
+2. Run the agent with a text prompt asking about weather
+3. The agent will invoke the GetWeather function tool to retrieve weather information
+4. Run the agent again with streaming to display the response as it's generated
+5. Clean up resources by deleting the agent
+
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
copilot-swe-agent[bot]androgerbarreto	b57c9f175b	fix: update PyRIT repository link from Azure/PyRIT to microsoft/PyRIT Agent-Logs-Url: https://github.com/microsoft/agent-framework/sessions/830b8ccf-a79c-49b6-90c9-3bb3e740bc06 Co-authored-by: rogerbarreto <19890735+rogerbarreto@users.noreply.github.com>	2026-03-28 15:43:29 +00:00
copilot-swe-agent[bot]	a503a2a8a6	Initial plan	2026-03-28 15:42:25 +00:00