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
666 changed files with 26087 additions and 25781 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/)
@@ -94,23 +94,23 @@ Create a simple Azure Responses Agent that writes a haiku about the Microsoft Ag
 # Use `az login` to authenticate with Azure CLI
 import os
 import asyncio
-from agent_framework import Agent
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from azure.identity import AzureCliCredential


 async def main():
-    # Initialize a chat agent with Microsoft Foundry
+    # Initialize a chat agent with Azure OpenAI Responses
    # the endpoint, deployment name, and api version can be set via environment variables
-    # or they can be passed in directly to the FoundryChatClient constructor
-    agent = Agent(
-      client=FoundryChatClient(
-          credential=AzureCliCredential(),
-          # project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-          # model=os.environ["FOUNDRY_MODEL_DEPLOYMENT_NAME"],
-      ),
-      name="HaikuBot",
-      instructions="You are an upbeat assistant that writes beautifully.",
+    # or they can be passed in directly to the AzureOpenAIResponsesClient constructor
+    agent = AzureOpenAIResponsesClient(
+        # endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
+        # deployment_name=os.environ["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"],
+        # api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+        # api_key=os.environ["AZURE_OPENAI_API_KEY"],  # Optional if using AzureCliCredential
+        credential=AzureCliCredential(), # Optional, if using api_key
+    ).as_agent(
+        name="HaikuBot",
+        instructions="You are an upbeat assistant that writes beautifully.",
    )

    print(await agent.run("Write a haiku about Microsoft Agent Framework."))
@@ -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
@@ -1,213 +0,0 @@
---
-name: verify-samples-tool
-description: How to use the verify-samples tool to run, verify, and manage sample definitions in the Agent Framework repository. Use this when adding, updating, or running sample verification.
---
-
-# verify-samples Tool
-
-The `verify-samples` project (`dotnet/eng/verify-samples/`) is an automated tool that runs sample projects and verifies their output using deterministic checks and AI-powered verification.
-
-## Running verify-samples
-
-```bash
-cd dotnet
-
-# Run all samples across all categories
-dotnet run --project eng/verify-samples -- --log results.log --csv results.csv
-
-# Run a specific category
-dotnet run --project eng/verify-samples -- --category 02-agents --log results.log
-
-# Run specific samples by name
-dotnet run --project eng/verify-samples -- Agent_Step02_StructuredOutput Agent_Step09_AsFunctionTool
-
-# Control parallelism (default 8)
-dotnet run --project eng/verify-samples -- --parallel 8 --log results.log
-
-# Combine options
-dotnet run --project eng/verify-samples -- --category 03-workflows --parallel 4 --log results.log --csv results.csv
-```
-
-### Required Environment Variables
-
-The tool itself needs:
- `AZURE_OPENAI_ENDPOINT` — for the AI verification agent
- `AZURE_OPENAI_DEPLOYMENT_NAME` (optional, defaults to `gpt-5-mini`)
-
-Individual samples require their own env vars (e.g., `AZURE_AI_PROJECT_ENDPOINT`). The tool automatically checks and skips samples with missing env vars.
-
-### Output Files
-
- `--log results.log` — detailed per-sample log with stdout/stderr, AI reasoning, and a summary
- `--csv results.csv` — tabular summary with Sample, ProjectPath, Status, FailedChecks, and Failures columns
-
-## Sample Categories
-
-Definitions are in the `dotnet/eng/verify-samples/` directory:
-
-| Category | Config File | Registered Key |
-|----------|-------------|----------------|
-| 01-get-started | `GetStartedSamples.cs` | `01-get-started` |
-| 02-agents | `AgentsSamples.cs` | `02-agents` |
-| 03-workflows | `WorkflowSamples.cs` | `03-workflows` |
-
-Categories are registered in `VerifyOptions.cs` in the `s_sampleSets` dictionary.
-
-## SampleDefinition Properties
-
-Each sample is defined as a `SampleDefinition` in the appropriate config file. Key properties:
-
-```csharp
-new SampleDefinition
-{
-    // Required: Display name for the sample
-    Name = "Agent_Step02_StructuredOutput",
-
-    // Required: Relative path from dotnet/ to the sample project directory
-    ProjectPath = "samples/02-agents/Agents/Agent_Step02_StructuredOutput",
-
-    // Environment variables the sample requires (throws if missing)
-    RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-
-    // Environment variables with defaults that would prompt on console if unset
-    OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-
-    // Skip this sample with a reason (for structural issues only)
-    SkipReason = null, // or "Requires external service X."
-
-    // Deterministic checks: substrings that must appear in stdout
-    MustContain = ["=== Section Header ==="],
-
-    // Substrings that must NOT appear in stdout
-    MustNotContain = [],
-
-    // If true, only MustContain checks are used (no AI verification)
-    IsDeterministic = false,
-
-    // AI verification: natural-language descriptions of expected output
-    // Each entry describes one aspect to verify independently
-    ExpectedOutputDescription =
-    [
-        "The output should show structured person information with Name, Age, and Occupation fields.",
-        "The output should not contain error messages or stack traces.",
-    ],
-
-    // Stdin inputs to feed to the sample (for interactive samples)
-    Inputs = ["Y", "Y", "Y"],
-
-    // Delay between stdin inputs in ms (default 2000, increase for LLM calls between inputs)
-    InputDelayMs = 3000,
-}
-```
-
-## How to Add a New Sample Definition
-
-1. **Check the sample's Program.cs** to understand:
-   - What environment variables it reads (look for `GetEnvironmentVariable`)
-   - Whether it needs stdin input (look for `Console.ReadLine`, `Application.GetInput`)
-   - Whether it has an external loop (look for `EXIT` patterns in YAML workflows)
-   - What output it produces (section headers, markers, expected behavior)
-   - Whether it exits on its own or runs as a server
-
-2. **Choose the right verification strategy:**
-   - **Deterministic** (`IsDeterministic = true`): Use `MustContain` for samples with fixed output strings. No AI verification.
-   - **AI-verified** (default): Use `ExpectedOutputDescription` with semantic descriptions. Write expectations that are flexible enough for non-deterministic LLM output.
-   - **Both**: Use `MustContain` for fixed markers AND `ExpectedOutputDescription` for LLM-generated content.
-
-3. **Set `SkipReason` only for structural issues:**
-   - Web servers that don't exit
-   - Multi-process client/server architectures
-   - Samples requiring external infrastructure (MCP servers you can't reach, Docker, etc.)
-   - Do NOT skip for missing env vars — the tool checks those dynamically.
-
-4. **For interactive samples, provide `Inputs`:**
-   - Samples using `Application.GetInput(args)` need one initial input
-   - Samples with `Console.ReadLine()` approval loops need `"Y"` inputs
-   - YAML workflows with `externalLoop` need `"EXIT"` as the last input
-   - Set `InputDelayMs` to 3000-8000ms for samples with LLM calls between inputs
-
-5. **Add the definition** to the appropriate config file (e.g., `AgentsSamples.cs`) in the `All` list.
-
-6. **Register new categories** (if needed) in `VerifyOptions.cs` `s_sampleSets` dictionary.
-
-### Writing Good ExpectedOutputDescription
-
- Write descriptions that are **semantically flexible** — LLM output varies between runs
- Each array entry should describe **one independent aspect** to verify
- Always include `"The output should not contain error messages or stack traces."` as the last entry
- Avoid exact wording expectations — use "should mention", "should contain information about", "should show"
- Bad: `"The output should say 'The weather in Amsterdam is cloudy with a high of 15°C'"`
- Good: `"The output should contain weather information about Amsterdam mentioning cloudy weather with a high of 15°C."`
-
-### Example: Simple LLM Sample
-
-```csharp
-new SampleDefinition
-{
-    Name = "Agent_With_AzureOpenAIChatCompletion",
-    ProjectPath = "samples/02-agents/AgentProviders/Agent_With_AzureOpenAIChatCompletion",
-    RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-    OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-    ExpectedOutputDescription =
-    [
-        "The output should contain a joke about a pirate.",
-        "The output should not contain error messages or stack traces.",
-    ],
-},
-```
-
-### Example: Deterministic Sample
-
-```csharp
-new SampleDefinition
-{
-    Name = "Workflow_Declarative_GenerateCode",
-    ProjectPath = "samples/03-workflows/Declarative/GenerateCode",
-    IsDeterministic = true,
-    MustContain = ["WORKFLOW: Parsing", "WORKFLOW: Defined"],
-    ExpectedOutputDescription = ["The output should show a YAML workflow being parsed and C# code being generated from it."],
-},
-```
-
-### Example: Interactive Sample with Approval Loop
-
-```csharp
-new SampleDefinition
-{
-    Name = "FoundryAgent_Hosted_MCP",
-    ProjectPath = "samples/02-agents/ModelContextProtocol/FoundryAgent_Hosted_MCP",
-    RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-    OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-    Inputs = ["Y", "Y", "Y", "Y", "Y"],
-    InputDelayMs = 5000,
-    ExpectedOutputDescription = ["The output should show an agent using the Microsoft Learn MCP tool with approval prompts."],
-},
-```
-
-### Example: Declarative Workflow with External Loop
-
-```csharp
-new SampleDefinition
-{
-    Name = "Workflow_Declarative_FunctionTools",
-    ProjectPath = "samples/03-workflows/Declarative/FunctionTools",
-    RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-    OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-    Inputs = ["What are today's specials?", "EXIT"],
-    InputDelayMs = 8000,
-    ExpectedOutputDescription = ["The output should show a workflow calling function tools to answer a question about restaurant specials."],
-},
-```
-
-### Example: Skipped Sample
-
-```csharp
-new SampleDefinition
-{
-    Name = "Agent_MCP_Server",
-    ProjectPath = "samples/02-agents/ModelContextProtocol/Agent_MCP_Server",
-    RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-    OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-    SkipReason = "Runs as an MCP stdio server that does not exit on its own.",
-},
-```
@@ -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" />
@@ -7,7 +7,6 @@
  <Folder Name="/Samples/">
    <File Path="samples/AGENTS.md" />
    <File Path="samples/README.md" />
-    <Project Path="eng/verify-samples/verify-samples.csproj" />
  </Folder>
  <Folder Name="/Samples/01-get-started/">
    <Project Path="samples/01-get-started/01_hello_agent/01_hello_agent.csproj" />
@@ -106,7 +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" />
@@ -123,34 +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" />
@@ -172,7 +142,35 @@
    <Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step02_CustomVectorStoreRAG/AgentWithRAG_Step02_CustomVectorStoreRAG.csproj" />
    <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" />
-    <Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step05_Neo4jGraphRAG/AgentWithRAG_Step05_Neo4jGraphRAG.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" />
@@ -320,8 +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" />
@@ -1,95 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-namespace VerifySamples;
-
-/// <summary>
-/// Thread-safe console output with sample-name prefixes and colored status.
-/// </summary>
-internal sealed class ConsoleReporter
-{
-    private readonly object _lock = new();
-
-    /// <summary>
-    /// Writes a complete prefixed line atomically to the console.
-    /// </summary>
-    public void WriteLineWithPrefix(string sampleName, string message, ConsoleColor? color = null)
-    {
-        lock (this._lock)
-        {
-            Console.ForegroundColor = ConsoleColor.Cyan;
-            Console.Write($"[{sampleName}] ");
-            if (color.HasValue)
-            {
-                Console.ForegroundColor = color.Value;
-            }
-            else
-            {
-                Console.ResetColor();
-            }
-
-            Console.WriteLine(message);
-            Console.ResetColor();
-        }
-    }
-
-    /// <summary>
-    /// Prints the final summary table and elapsed time to the console.
-    /// </summary>
-    public void PrintSummary(
-        IReadOnlyList<VerificationResult> orderedResults,
-        IReadOnlyList<(string Name, string Reason)> skipped,
-        TimeSpan elapsed)
-    {
-        var passCount = orderedResults.Count(r => r.Passed);
-        var failCount = orderedResults.Count(r => !r.Passed);
-
-        Console.WriteLine();
-        Console.WriteLine(new string('─', 60));
-        Console.ForegroundColor = ConsoleColor.White;
-        Console.WriteLine("SUMMARY");
-        Console.ResetColor();
-
-        foreach (var result in orderedResults)
-        {
-            Console.ForegroundColor = result.Passed ? ConsoleColor.Green : ConsoleColor.Red;
-            Console.Write(result.Passed ? "  ✓ " : "  ✗ ");
-            Console.ResetColor();
-            Console.WriteLine($"{result.SampleName}: {result.Summary}");
-        }
-
-        foreach (var (name, reason) in skipped)
-        {
-            Console.ForegroundColor = ConsoleColor.Yellow;
-            Console.Write("  ○ ");
-            Console.ResetColor();
-            Console.WriteLine($"{name}: Skipped — {reason}");
-        }
-
-        Console.WriteLine();
-        Console.Write("Results: ");
-        Console.ForegroundColor = ConsoleColor.Green;
-        Console.Write($"{passCount} passed");
-        Console.ResetColor();
-
-        if (failCount > 0)
-        {
-            Console.Write(", ");
-            Console.ForegroundColor = ConsoleColor.Red;
-            Console.Write($"{failCount} failed");
-            Console.ResetColor();
-        }
-
-        if (skipped.Count > 0)
-        {
-            Console.Write(", ");
-            Console.ForegroundColor = ConsoleColor.Yellow;
-            Console.Write($"{skipped.Count} skipped");
-            Console.ResetColor();
-        }
-
-        Console.WriteLine();
-        Console.ForegroundColor = ConsoleColor.DarkGray;
-        Console.WriteLine($"Elapsed: {elapsed.Hours:D2}:{elapsed.Minutes:D2}:{elapsed.Seconds:D2}");
-        Console.ResetColor();
-    }
-}
@@ -1,56 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.Text;
-
-namespace VerifySamples;
-
-/// <summary>
-/// Writes a CSV summary of sample verification results.
-/// </summary>
-internal static class CsvResultWriter
-{
-    /// <summary>
-    /// Writes the results to a CSV file at the specified path.
-    /// </summary>
-    public static async Task WriteAsync(
-        string path,
-        IReadOnlyList<VerificationResult> orderedResults,
-        IReadOnlyList<(string Name, string Reason)> skipped,
-        IReadOnlyList<SampleDefinition> samples)
-    {
-        var pathLookup = samples.ToDictionary(s => s.Name, s => s.ProjectPath);
-
-        var sb = new StringBuilder();
-        sb.AppendLine("Sample,ProjectPath,Status,FailedChecks,Failures");
-
-        foreach (var result in orderedResults)
-        {
-            var status = result.Passed ? "PASSED" : "FAILED";
-            var failedChecks = result.Failures.Count;
-            var failures = string.Join("; ", result.Failures);
-            pathLookup.TryGetValue(result.SampleName, out var projectPath);
-            sb.AppendLine($"{CsvEscape(result.SampleName)},{CsvEscape(projectPath ?? "")},{status},{failedChecks},{CsvEscape(failures)}");
-        }
-
-        foreach (var (name, reason) in skipped)
-        {
-            pathLookup.TryGetValue(name, out var projectPath);
-            sb.AppendLine($"{CsvEscape(name)},{CsvEscape(projectPath ?? "")},SKIPPED,0,{CsvEscape(reason)}");
-        }
-
-        await File.WriteAllTextAsync(path, sb.ToString());
-    }
-
-    /// <summary>
-    /// Escapes a value for CSV: wraps in quotes if it contains commas, quotes, or newlines.
-    /// </summary>
-    private static string CsvEscape(string value)
-    {
-        if (value.Contains('"') || value.Contains(',') || value.Contains('\n') || value.Contains('\r'))
-        {
-            return $"\"{value.Replace("\"", "\"\"")}\"";
-        }
-
-        return value;
-    }
-}
@@ -1,105 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-namespace VerifySamples;
-
-/// <summary>
-/// Defines the expected behavior for each sample in 01-get-started.
-/// </summary>
-internal static class GetStartedSamples
-{
-    public static IReadOnlyList<SampleDefinition> All { get; } =
-    [
-        new SampleDefinition
-        {
-            Name = "05_first_workflow",
-            ProjectPath = "samples/01-get-started/05_first_workflow",
-            RequiredEnvironmentVariables = [],
-            IsDeterministic = true,
-            MustContain =
-            [
-                "UppercaseExecutor: HELLO, WORLD!",
-                "ReverseTextExecutor: !DLROW ,OLLEH",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "01_hello_agent",
-            ProjectPath = "samples/01-get-started/01_hello_agent",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            ExpectedOutputDescription =
-            [
-                "The output should contain a joke about a pirate.",
-                "There should be two separate joke responses — one from a non-streaming call and one from a streaming call.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "02_add_tools",
-            ProjectPath = "samples/01-get-started/02_add_tools",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            MustContain = [],
-            ExpectedOutputDescription =
-            [
-                "The output should contain information about the weather in Amsterdam.",
-                "The response should mention that it is cloudy with a high of 15°C (or equivalent), since this comes from a tool that returns a canned response.",
-                "There should be two responses — one from a non-streaming call and one from a streaming call.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "03_multi_turn",
-            ProjectPath = "samples/01-get-started/03_multi_turn",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            ExpectedOutputDescription =
-            [
-                "The output should contain a joke about a pirate.",
-                "After the initial joke, there should be a modified version that includes emojis and is told in the voice of a pirate's parrot.",
-                "The pattern repeats: first a non-streaming pirate joke + parrot version, then a streaming pirate joke + parrot version.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "04_memory",
-            ProjectPath = "samples/01-get-started/04_memory",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            MustContain =
-            [
-                ">> Use session with blank memory",
-                ">> Use deserialized session with previously created memories",
-                ">> Read memories using memory component",
-                "MEMORY - User Name:",
-                "MEMORY - User Age:",
-                ">> Use new session with previously created memories",
-            ],
-            ExpectedOutputDescription =
-            [
-                "In the 'Use session with blank memory' section, the agent should respond to the user's messages. It may ask for the user's name or age if not yet known.",
-                "In the 'Use deserialized session with previously created memories' section, the agent should correctly recall that the user's name is Ruaidhrí and age is 20.",
-                "The 'MEMORY - User Name:' line should show 'Ruaidhrí' (or a close transliteration).",
-                "The 'MEMORY - User Age:' line should show '20'.",
-                "In the 'Use new session with previously created memories' section, the agent should know the user's name and age from the transferred memory.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "06_host_your_agent",
-            ProjectPath = "samples/01-get-started/06_host_your_agent",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            SkipReason = "Requires Azure Functions Core Tools runtime and starts a web server.",
-        },
-    ];
-}
@@ -1,153 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.Text;
-
-namespace VerifySamples;
-
-/// <summary>
-/// Incrementally writes a sequential (non-interleaved) log file, appending after each sample completes.
-/// Thread-safe: multiple parallel tasks may call write methods concurrently.
-/// </summary>
-internal sealed class LogFileWriter : IDisposable
-{
-    private readonly string _path;
-    private readonly SemaphoreSlim _writeLock = new(1, 1);
-
-    public LogFileWriter(string path)
-    {
-        this._path = path;
-    }
-
-    /// <inheritdoc />
-    public void Dispose()
-    {
-        this._writeLock.Dispose();
-    }
-
-    /// <summary>
-    /// Writes the log file header. Call once at the start of the run.
-    /// </summary>
-    public async Task WriteHeaderAsync()
-    {
-        var sb = new StringBuilder();
-        sb.AppendLine($"Sample Verification Log — {DateTime.UtcNow:yyyy-MM-dd HH:mm:ss} UTC");
-        sb.AppendLine(new string('═', 72));
-        sb.AppendLine();
-
-        await File.WriteAllTextAsync(this._path, sb.ToString());
-    }
-
-    /// <summary>
-    /// Appends a skipped-sample entry to the log file.
-    /// </summary>
-    public async Task WriteSkippedAsync(string name, string reason)
-    {
-        var sb = new StringBuilder();
-        sb.AppendLine($"── {name} ──");
-        sb.AppendLine($"Status: SKIPPED — {reason}");
-        sb.AppendLine();
-
-        await this.AppendAsync(sb.ToString());
-    }
-
-    /// <summary>
-    /// Appends a completed sample's full output section to the log file.
-    /// </summary>
-    public async Task WriteSampleResultAsync(VerificationResult result)
-    {
-        var sb = new StringBuilder();
-        sb.AppendLine(new string('─', 72));
-        sb.AppendLine($"── {result.SampleName} ──");
-        sb.AppendLine($"Status: {(result.Passed ? "PASSED" : "FAILED")}");
-        sb.AppendLine();
-
-        foreach (var line in result.LogLines)
-        {
-            sb.AppendLine(line);
-        }
-
-        sb.AppendLine();
-
-        if (!string.IsNullOrWhiteSpace(result.Stdout))
-        {
-            sb.AppendLine("--- stdout ---");
-            sb.AppendLine(result.Stdout.TrimEnd());
-            sb.AppendLine("--- end stdout ---");
-            sb.AppendLine();
-        }
-
-        if (!string.IsNullOrWhiteSpace(result.Stderr))
-        {
-            sb.AppendLine("--- stderr ---");
-            sb.AppendLine(result.Stderr.TrimEnd());
-            sb.AppendLine("--- end stderr ---");
-            sb.AppendLine();
-        }
-
-        if (result.Failures.Count > 0)
-        {
-            sb.AppendLine("Failures:");
-            foreach (var failure in result.Failures)
-            {
-                sb.AppendLine($"  ✗ {failure}");
-            }
-
-            sb.AppendLine();
-        }
-
-        if (result.AIReasoning is not null)
-        {
-            sb.AppendLine("AI Reasoning:");
-            sb.AppendLine(result.AIReasoning);
-            sb.AppendLine();
-        }
-
-        await this.AppendAsync(sb.ToString());
-    }
-
-    /// <summary>
-    /// Appends the final summary section and elapsed time to the log file.
-    /// </summary>
-    public async Task WriteSummaryAsync(
-        IReadOnlyList<VerificationResult> orderedResults,
-        IReadOnlyList<(string Name, string Reason)> skipped,
-        TimeSpan elapsed)
-    {
-        var passCount = orderedResults.Count(r => r.Passed);
-        var failCount = orderedResults.Count(r => !r.Passed);
-
-        var sb = new StringBuilder();
-        sb.AppendLine(new string('═', 72));
-        sb.AppendLine("SUMMARY");
-        sb.AppendLine();
-
-        foreach (var result in orderedResults)
-        {
-            sb.AppendLine($"  {(result.Passed ? "✓" : "✗")} {result.SampleName}: {result.Summary}");
-        }
-
-        foreach (var (name, reason) in skipped)
-        {
-            sb.AppendLine($"  ○ {name}: Skipped — {reason}");
-        }
-
-        sb.AppendLine();
-        sb.AppendLine($"Results: {passCount} passed{(failCount > 0 ? $", {failCount} failed" : "")}{(skipped.Count > 0 ? $", {skipped.Count} skipped" : "")}");
-        sb.AppendLine($"Elapsed: {elapsed.Hours:D2}:{elapsed.Minutes:D2}:{elapsed.Seconds:D2}");
-
-        await this.AppendAsync(sb.ToString());
-    }
-
-    private async Task AppendAsync(string text)
-    {
-        await this._writeLock.WaitAsync();
-        try
-        {
-            await File.AppendAllTextAsync(this._path, text);
-        }
-        finally
-        {
-            this._writeLock.Release();
-        }
-    }
-}
@@ -1,98 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This tool runs the 01-get-started, 02-agents, and 03-workflows samples and verifies their output.
-// Deterministic samples are verified with exact string matching.
-// Non-deterministic (LLM) samples are verified using an agent-framework agent.
-//
-// Usage:
-//   dotnet run                                          # Run all samples
-//   dotnet run -- 01_hello_agent 05_first_workflow      # Run specific samples by name
-//   dotnet run -- --category 01-get-started             # Run the 01-get-started category
-//   dotnet run -- --category 02-agents                  # Run the 02-agents category
-//   dotnet run -- --category 03-workflows               # Run the 03-workflows category
-//   dotnet run -- --parallel 16                         # Run up to 16 samples concurrently
-//   dotnet run -- --log results.log                     # Write sequential log to file
-//   dotnet run -- --csv results.csv                     # Write CSV summary to file
-//
-// Required environment variables (for AI-powered samples):
-//   AZURE_OPENAI_ENDPOINT
-//   AZURE_OPENAI_DEPLOYMENT_NAME (optional, defaults to gpt-5-mini)
-
-using System.Diagnostics;
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using VerifySamples;
-
-var options = VerifyOptions.Parse(args);
-if (options is null)
-{
-    return 1;
-}
-
-var stopwatch = Stopwatch.StartNew();
-
-// Resolve the dotnet/ root directory (verify-samples is at dotnet/eng/verify-samples/)
-var dotnetRoot = Path.GetFullPath(Path.Combine(AppContext.BaseDirectory, "..", "..", "..", "..", ".."));
-if (!File.Exists(Path.Combine(dotnetRoot, "agent-framework-dotnet.slnx")))
-{
-    dotnetRoot = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), "..", ".."));
-}
-
-// Set up the AI verifier
-var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT");
-var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5-mini";
-
-OpenAI.Chat.ChatClient? chatClient = null;
-if (!string.IsNullOrEmpty(endpoint))
-{
-    chatClient = new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential())
-        .GetChatClient(deploymentName);
-}
-
-// Set up optional log file writer
-LogFileWriter? logWriter = null;
-if (options.LogFilePath is not null)
-{
-    logWriter = new LogFileWriter(options.LogFilePath);
-    await logWriter.WriteHeaderAsync();
-}
-
-try
-{
-    // Run all samples
-    var reporter = new ConsoleReporter();
-    var verifier = new SampleVerifier(chatClient);
-    var orchestrator = new VerificationOrchestrator(verifier, reporter, dotnetRoot, TimeSpan.FromMinutes(3), logWriter);
-
-    var run = await orchestrator.RunAllAsync(options.Samples, options.MaxParallelism);
-
-    stopwatch.Stop();
-
-    // Print summary
-    var orderedResults = run.SampleOrder
-        .Where(run.Results.ContainsKey)
-        .Select(name => run.Results[name])
-        .ToList();
-
-    reporter.PrintSummary(orderedResults, run.Skipped, stopwatch.Elapsed);
-
-    // Write log file summary
-    if (logWriter is not null)
-    {
-        await logWriter.WriteSummaryAsync(orderedResults, run.Skipped, stopwatch.Elapsed);
-        Console.WriteLine($"Log written to: {options.LogFilePath}");
-    }
-
-    // Write CSV summary
-    if (options.CsvFilePath is not null)
-    {
-        await CsvResultWriter.WriteAsync(options.CsvFilePath, orderedResults, run.Skipped, options.Samples);
-        Console.WriteLine($"CSV written to: {options.CsvFilePath}");
-    }
-
-    return orderedResults.Any(r => !r.Passed) ? 1 : 0;
-}
-finally
-{
-    logWriter?.Dispose();
-}
@@ -1,79 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-namespace VerifySamples;
-
-/// <summary>
-/// Describes a sample to verify, including its expected output.
-/// </summary>
-internal sealed class SampleDefinition
-{
-    /// <summary>
-    /// Display name for the sample (e.g., "01_hello_agent").
-    /// </summary>
-    public required string Name { get; init; }
-
-    /// <summary>
-    /// Relative path from the dotnet/ directory to the sample project directory.
-    /// </summary>
-    public required string ProjectPath { get; init; }
-
-    /// <summary>
-    /// Environment variables that the sample requires for a meaningful run.
-    /// The runner checks these before running and will skip the sample if any are unset,
-    /// recording a skip reason that indicates which required variables are missing.
-    /// </summary>
-    public string[] RequiredEnvironmentVariables { get; init; } = [];
-
-    /// <summary>
-    /// Environment variables that the sample can use but typically has fallbacks or defaults for.
-    /// If these are not set, the sample might prompt or behave interactively, which could cause
-    /// automated verification to hang. The runner checks these and skips the sample if they are unset
-    /// to avoid non-deterministic or blocking behavior in automated runs.
-    /// </summary>
-    public string[] OptionalEnvironmentVariables { get; init; } = [];
-
-    /// <summary>
-    /// If set, the sample is skipped with this reason.
-    /// Use only for structural reasons (e.g., web server, multi-process, needs external service).
-    /// Do NOT use for missing environment variables — those are checked dynamically.
-    /// </summary>
-    public string? SkipReason { get; init; }
-
-    /// <summary>
-    /// Substrings that must appear in stdout for the sample to pass.
-    /// Used for deterministic verification.
-    /// </summary>
-    public string[] MustContain { get; init; } = [];
-
-    /// <summary>
-    /// Substrings that must not appear in stdout for the sample to pass.
-    /// </summary>
-    public string[] MustNotContain { get; init; } = [];
-
-    /// <summary>
-    /// If true, <see cref="MustContain"/> entries cover the entire expected output —
-    /// no AI verification is needed.
-    /// </summary>
-    public bool IsDeterministic { get; init; }
-
-    /// <summary>
-    /// Natural-language description of what the sample output should look like.
-    /// Used by the AI verifier for non-deterministic samples.
-    /// Each entry describes one aspect of the expected output that should be verified.
-    /// </summary>
-    public string[] ExpectedOutputDescription { get; init; } = [];
-
-    /// <summary>
-    /// Sequence of stdin inputs to feed to the sample process.
-    /// Each entry is written as a line (followed by newline) to the process stdin.
-    /// A <c>null</c> entry inserts a delay without writing anything.
-    /// Inputs are sent with a short delay between each to allow the process to prompt.
-    /// </summary>
-    public string?[] Inputs { get; init; } = [];
-
-    /// <summary>
-    /// Delay in milliseconds between each input line. Default is 2000ms.
-    /// Increase for samples that need more time between prompts (e.g., LLM calls between inputs).
-    /// </summary>
-    public int InputDelayMs { get; init; } = 2000;
-}
@@ -1,132 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.Diagnostics;
-
-namespace VerifySamples;
-
-/// <summary>
-/// Result of running a sample process.
-/// </summary>
-internal sealed record SampleRunResult(
-    string Stdout,
-    string Stderr,
-    int ExitCode,
-    TimeSpan Elapsed);
-
-/// <summary>
-/// Runs a sample project via <c>dotnet run</c> and captures its output.
-/// </summary>
-internal static class SampleRunner
-{
-    /// <summary>
-    /// Runs <c>dotnet run --framework net10.0</c> in the given project directory.
-    /// </summary>
-    public static Task<SampleRunResult> RunAsync(
-        string projectPath,
-        TimeSpan timeout,
-        CancellationToken cancellationToken = default)
-        => RunAsync(projectPath, "run --framework net10.0", timeout, inputs: null, inputDelayMs: 0, cancellationToken: cancellationToken);
-
-    /// <summary>
-    /// Runs <c>dotnet run --framework net10.0</c> with stdin inputs.
-    /// </summary>
-    public static Task<SampleRunResult> RunAsync(
-        string projectPath,
-        TimeSpan timeout,
-        string?[]? inputs,
-        int inputDelayMs = 2000,
-        CancellationToken cancellationToken = default)
-        => RunAsync(projectPath, "run --framework net10.0", timeout, inputs, inputDelayMs, cancellationToken);
-
-    /// <summary>
-    /// Runs an arbitrary <c>dotnet</c> command in the given working directory.
-    /// </summary>
-    public static async Task<SampleRunResult> RunAsync(
-        string workingDirectory,
-        string dotnetArgs,
-        TimeSpan timeout,
-        string?[]? inputs = null,
-        int inputDelayMs = 0,
-        CancellationToken cancellationToken = default)
-    {
-        var psi = new ProcessStartInfo
-        {
-            FileName = "dotnet",
-            Arguments = dotnetArgs,
-            WorkingDirectory = workingDirectory,
-            RedirectStandardOutput = true,
-            RedirectStandardError = true,
-            RedirectStandardInput = inputs is { Length: > 0 },
-            UseShellExecute = false,
-            CreateNoWindow = true,
-        };
-
-        var sw = Stopwatch.StartNew();
-
-        using var process = new Process { StartInfo = psi };
-        process.Start();
-
-        var stdoutTask = process.StandardOutput.ReadToEndAsync(cancellationToken);
-        var stderrTask = process.StandardError.ReadToEndAsync(cancellationToken);
-
-        // Feed stdin inputs with delays if configured
-        if (inputs is { Length: > 0 })
-        {
-            _ = Task.Run(async () =>
-            {
-                try
-                {
-                    foreach (var input in inputs)
-                    {
-                        await Task.Delay(inputDelayMs, cancellationToken);
-                        if (input is not null)
-                        {
-                            await process.StandardInput.WriteLineAsync(input.AsMemory(), cancellationToken);
-                            await process.StandardInput.FlushAsync(cancellationToken);
-                        }
-                    }
-
-                    process.StandardInput.Close();
-                }
-                catch (Exception ex) when (ex is IOException or ObjectDisposedException or OperationCanceledException)
-                {
-                    // Process may have exited before all inputs were sent
-                }
-            }, cancellationToken);
-        }
-
-        using var cts = CancellationTokenSource.CreateLinkedTokenSource(cancellationToken);
-        cts.CancelAfter(timeout);
-
-        try
-        {
-            await process.WaitForExitAsync(cts.Token);
-        }
-        catch (OperationCanceledException) when (!cancellationToken.IsCancellationRequested)
-        {
-            // Timeout — kill the process
-            try
-            {
-                process.Kill(entireProcessTree: true);
-            }
-            catch
-            {
-                // Best effort
-            }
-
-            sw.Stop();
-            return new SampleRunResult(
-                Stdout: await stdoutTask,
-                Stderr: $"TIMEOUT: Sample did not complete within {timeout.TotalSeconds}s.\n{await stderrTask}",
-                ExitCode: -1,
-                Elapsed: sw.Elapsed);
-        }
-
-        sw.Stop();
-        return new SampleRunResult(
-            Stdout: await stdoutTask,
-            Stderr: await stderrTask,
-            ExitCode: process.ExitCode,
-            Elapsed: sw.Elapsed);
-    }
-}
@@ -1,202 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.Text.Json.Serialization;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-using OpenAI.Chat;
-
-namespace VerifySamples;
-
-/// <summary>
-/// Verifies sample output using deterministic checks and an AI agent
-/// for non-deterministic output validation.
-/// </summary>
-internal sealed class SampleVerifier
-{
-    private readonly AIAgent? _verifierAgent;
-
-    /// <summary>
-    /// Creates a verifier. If <paramref name="chatClient"/> is provided,
-    /// AI-based verification is available for non-deterministic samples.
-    /// </summary>
-    public SampleVerifier(ChatClient? chatClient = null)
-    {
-        if (chatClient is not null)
-        {
-            this._verifierAgent = chatClient.AsAIAgent(
-                instructions: """
-                    You are a test output verifier. You will be given:
-                    1. The actual stdout output of a program
-                    2. A list of expectations about what the output should contain or demonstrate
-
-                    Your job is to determine whether the actual output satisfies each expectation.
-                    Be reasonable — the output comes from an LLM so exact wording won't match, but the
-                    semantic intent should be clearly satisfied.
-                    """,
-                name: "OutputVerifier");
-        }
-    }
-
-    /// <summary>
-    /// Verifies the output of a sample run against its definition.
-    /// </summary>
-    public async Task<VerificationResult> VerifyAsync(SampleDefinition sample, SampleRunResult run)
-    {
-        var failures = new List<string>();
-
-        // 1. Exit code check
-        if (run.ExitCode != 0)
-        {
-            failures.Add($"Exit code was {run.ExitCode}, expected 0. Stderr: {Truncate(run.Stderr, 500)}");
-        }
-
-        // 2. Must-contain checks
-        foreach (var expected in sample.MustContain)
-        {
-            if (!run.Stdout.Contains(expected, StringComparison.Ordinal))
-            {
-                failures.Add($"Output missing expected substring: \"{expected}\"");
-            }
-        }
-
-        // 3. Must-not-contain checks
-        foreach (var unexpected in sample.MustNotContain)
-        {
-            if (run.Stdout.Contains(unexpected, StringComparison.Ordinal))
-            {
-                failures.Add($"Output contains unexpected substring: \"{unexpected}\"");
-            }
-        }
-
-        // 4. AI verification for non-deterministic samples
-        string? aiReasoning = null;
-        if (!sample.IsDeterministic && sample.ExpectedOutputDescription.Length > 0)
-        {
-            if (this._verifierAgent is null)
-            {
-                failures.Add("AI verification required but no AI agent configured (missing AZURE_OPENAI_ENDPOINT).");
-            }
-            else
-            {
-                var aiResult = await this.VerifyWithAIAsync(run.Stdout, sample.ExpectedOutputDescription);
-                aiReasoning = aiResult.Reasoning;
-
-                foreach (var unmet in aiResult.UnmetExpectations)
-                {
-                    failures.Add($"AI expectation not met: {unmet}");
-                }
-            }
-        }
-
-        bool passed = failures.Count == 0;
-        return new VerificationResult
-        {
-            SampleName = sample.Name,
-            Passed = passed,
-            Summary = passed ? "All checks passed" : $"{failures.Count} check(s) failed",
-            Failures = failures,
-            AIReasoning = aiReasoning,
-        };
-    }
-
-    private async Task<(string Reasoning, List<string> UnmetExpectations)> VerifyWithAIAsync(
-        string actualOutput,
-        string[] expectations)
-    {
-        var expectationList = string.Join("\n", expectations.Select((e, i) => $"  {i + 1}. {e}"));
-        var prompt = $"""
-            Actual program output:
-            ---
-            {Truncate(actualOutput, 4000)}
-            ---
-
-            Expectations to verify:
-            {expectationList}
-
-            Does the output satisfy all expectations?
-            """;
-
-        try
-        {
-            var response = await this._verifierAgent!.RunAsync<AIVerificationResponse>(prompt);
-            var result = response.Result;
-
-            if (result is null)
-            {
-                return ($"AI verification returned null result. Raw: {response.Text}", ["AI verification returned null result."]);
-            }
-
-            var reasoning = result.Reasoning ?? "(no reasoning provided)";
-
-            // Collect unmet expectations as individual failures
-            var unmet = new List<string>();
-            if (result.ExpectationResults is { Count: > 0 })
-            {
-                foreach (var er in result.ExpectationResults.Where(er => !er.Met))
-                {
-                    var detail = string.IsNullOrWhiteSpace(er.Detail) ? er.Expectation : $"{er.Expectation} — {er.Detail}";
-                    unmet.Add(detail ?? "Unknown expectation");
-                }
-
-                // If the model flagged overall failure but all individual expectations were met,
-                // still treat as failure using the overall reasoning.
-                if (unmet.Count == 0 && !result.Pass)
-                {
-                    unmet.Add(reasoning);
-                }
-            }
-            else if (!result.Pass)
-            {
-                // Fallback: no per-expectation detail but overall pass is false
-                unmet.Add(reasoning);
-            }
-
-            return (reasoning, unmet);
-        }
-        catch (Exception ex)
-        {
-            return ($"AI verification error: {ex.Message}", [$"AI verification error: {ex.Message}"]);
-        }
-    }
-
-    private static string Truncate(string text, int maxLength)
-        => text.Length <= maxLength ? text : text[..maxLength] + "... (truncated)";
-}
-
-/// <summary>
-/// Structured response from the AI verification agent.
-/// </summary>
-[System.Diagnostics.CodeAnalysis.SuppressMessage("Performance", "CA1812:Avoid uninstantiated internal classes", Justification = "Instantiated by JSON deserialization via RunAsync<T>.")]
-internal sealed class AIVerificationResponse
-{
-    /// <summary>Whether all expectations were met.</summary>
-    [JsonPropertyName("pass")]
-    public bool Pass { get; set; }
-
-    /// <summary>Brief explanation of the overall assessment.</summary>
-    [JsonPropertyName("reasoning")]
-    public string? Reasoning { get; set; }
-
-    /// <summary>Per-expectation results.</summary>
-    [JsonPropertyName("expectation_results")]
-    public List<ExpectationResult>? ExpectationResults { get; set; }
-}
-
-/// <summary>
-/// Result for an individual expectation check.
-/// </summary>
-[System.Diagnostics.CodeAnalysis.SuppressMessage("Performance", "CA1812:Avoid uninstantiated internal classes", Justification = "Instantiated by JSON deserialization via RunAsync<T>.")]
-internal sealed class ExpectationResult
-{
-    /// <summary>The expectation text that was evaluated.</summary>
-    [JsonPropertyName("expectation")]
-    public string? Expectation { get; set; }
-
-    /// <summary>Whether this expectation was met.</summary>
-    [JsonPropertyName("met")]
-    public bool Met { get; set; }
-
-    /// <summary>Detail about how the expectation was or was not met.</summary>
-    [JsonPropertyName("detail")]
-    public string? Detail { get; set; }
-}
@@ -1,197 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using System.Collections.Concurrent;
-
-namespace VerifySamples;
-
-/// <summary>
-/// Orchestrates sample verification: filters, runs in parallel, and collects results.
-/// </summary>
-internal sealed class VerificationOrchestrator
-{
-    private readonly SampleVerifier _verifier;
-    private readonly ConsoleReporter _reporter;
-    private readonly LogFileWriter? _logWriter;
-    private readonly string _dotnetRoot;
-    private readonly TimeSpan _timeout;
-
-    public VerificationOrchestrator(
-        SampleVerifier verifier,
-        ConsoleReporter reporter,
-        string dotnetRoot,
-        TimeSpan timeout,
-        LogFileWriter? logWriter = null)
-    {
-        this._verifier = verifier;
-        this._reporter = reporter;
-        this._logWriter = logWriter;
-        this._dotnetRoot = dotnetRoot;
-        this._timeout = timeout;
-    }
-
-    /// <summary>
-    /// The result of running all samples through the orchestrator.
-    /// </summary>
-    internal sealed record RunAllResult(
-        ConcurrentDictionary<string, VerificationResult> Results,
-        List<(string Name, string Reason)> Skipped,
-        List<string> SampleOrder);
-
-    /// <summary>
-    /// Filters samples, runs the runnable ones in parallel, and returns all results.
-    /// </summary>
-    public async Task<RunAllResult> RunAllAsync(
-        IReadOnlyList<SampleDefinition> samples,
-        int maxParallelism)
-    {
-        var skipped = new List<(string Name, string Reason)>();
-        var runnableSamples = new List<SampleDefinition>();
-        var sampleOrder = new List<string>();
-
-        // Separate samples into skipped and runnable
-        foreach (var sample in samples)
-        {
-            sampleOrder.Add(sample.Name);
-
-            if (sample.SkipReason is not null)
-            {
-                skipped.Add((sample.Name, sample.SkipReason));
-                this._reporter.WriteLineWithPrefix(sample.Name, $"SKIPPED — {sample.SkipReason}", ConsoleColor.Yellow);
-
-                if (this._logWriter is not null)
-                {
-                    await this._logWriter.WriteSkippedAsync(sample.Name, sample.SkipReason);
-                }
-
-                continue;
-            }
-
-            var missingRequired = sample.RequiredEnvironmentVariables
-                .Where(v => string.IsNullOrEmpty(Environment.GetEnvironmentVariable(v)))
-                .ToList();
-
-            var missingOptional = sample.OptionalEnvironmentVariables
-                .Where(v => string.IsNullOrEmpty(Environment.GetEnvironmentVariable(v)))
-                .ToList();
-
-            if (missingRequired.Count > 0 || missingOptional.Count > 0)
-            {
-                var reasons = new List<string>();
-                if (missingRequired.Count > 0)
-                {
-                    reasons.Add($"Missing required: {string.Join(", ", missingRequired)}");
-                }
-
-                if (missingOptional.Count > 0)
-                {
-                    reasons.Add($"Missing optional (would cause console prompt hang): {string.Join(", ", missingOptional)}");
-                }
-
-                var skipReason = string.Join("; ", reasons);
-                skipped.Add((sample.Name, skipReason));
-                this._reporter.WriteLineWithPrefix(sample.Name, $"SKIPPED — {skipReason}", ConsoleColor.Yellow);
-
-                if (this._logWriter is not null)
-                {
-                    await this._logWriter.WriteSkippedAsync(sample.Name, skipReason);
-                }
-
-                continue;
-            }
-
-            runnableSamples.Add(sample);
-        }
-
-        // Run samples in parallel
-        var results = new ConcurrentDictionary<string, VerificationResult>();
-        var semaphore = new SemaphoreSlim(maxParallelism);
-
-        this._reporter.WriteLineWithPrefix(
-            "runner", $"Running {runnableSamples.Count} samples (max {maxParallelism} parallel)...");
-
-        try
-        {
-            var tasks = runnableSamples.Select(sample => this.RunSingleAsync(sample, results, semaphore)).ToArray();
-            await Task.WhenAll(tasks);
-        }
-        finally
-        {
-            semaphore.Dispose();
-        }
-
-        return new RunAllResult(results, skipped, sampleOrder);
-    }
-
-    private async Task RunSingleAsync(
-        SampleDefinition sample,
-        ConcurrentDictionary<string, VerificationResult> results,
-        SemaphoreSlim semaphore)
-    {
-        await semaphore.WaitAsync();
-        try
-        {
-            var log = new List<string>();
-            log.Add($"[{sample.Name}] Running...");
-            this._reporter.WriteLineWithPrefix(sample.Name, "Running...");
-
-            var projectPath = Path.Combine(this._dotnetRoot, sample.ProjectPath);
-            var run = sample.Inputs.Length > 0
-                ? await SampleRunner.RunAsync(projectPath, this._timeout, sample.Inputs, sample.InputDelayMs)
-                : await SampleRunner.RunAsync(projectPath, this._timeout);
-
-            log.Add($"[{sample.Name}] Completed ({run.Elapsed.TotalSeconds:F1}s, exit={run.ExitCode})");
-            this._reporter.WriteLineWithPrefix(
-                sample.Name, $"Completed ({run.Elapsed.TotalSeconds:F1}s, exit={run.ExitCode}). Verifying...");
-
-            var result = await this._verifier.VerifyAsync(sample, run);
-
-            if (result.Passed)
-            {
-                log.Add($"[{sample.Name}] PASSED");
-                this._reporter.WriteLineWithPrefix(sample.Name, "PASSED", ConsoleColor.Green);
-            }
-            else
-            {
-                log.Add($"[{sample.Name}] FAILED");
-                this._reporter.WriteLineWithPrefix(sample.Name, "FAILED", ConsoleColor.Red);
-                foreach (var failure in result.Failures)
-                {
-                    log.Add($"[{sample.Name}]   ✗ {failure}");
-                    this._reporter.WriteLineWithPrefix(sample.Name, $"  ✗ {failure}", ConsoleColor.Red);
-                }
-            }
-
-            if (result.AIReasoning is not null)
-            {
-                log.Add($"[{sample.Name}]   AI: {result.AIReasoning}");
-                this._reporter.WriteLineWithPrefix(
-                    sample.Name, $"  AI: {Truncate(result.AIReasoning, 300)}", ConsoleColor.DarkGray);
-            }
-
-            var verificationResult = new VerificationResult
-            {
-                SampleName = result.SampleName,
-                Passed = result.Passed,
-                Summary = result.Summary,
-                Failures = result.Failures,
-                AIReasoning = result.AIReasoning,
-                Stdout = run.Stdout,
-                Stderr = run.Stderr,
-                LogLines = log,
-            };
-            results[sample.Name] = verificationResult;
-
-            if (this._logWriter is not null)
-            {
-                await this._logWriter.WriteSampleResultAsync(verificationResult);
-            }
-        }
-        finally
-        {
-            semaphore.Release();
-        }
-    }
-
-    private static string Truncate(string text, int maxLength)
-        => text.Length <= maxLength ? text : text[..maxLength] + "...";
-}
@@ -1,31 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-namespace VerifySamples;
-
-/// <summary>
-/// The result of verifying a single sample.
-/// </summary>
-internal sealed class VerificationResult
-{
-    public required string SampleName { get; init; }
-    public required bool Passed { get; init; }
-    public required string Summary { get; init; }
-    public List<string> Failures { get; init; } = [];
-    public string? AIReasoning { get; init; }
-
-    /// <summary>
-    /// The sample's stdout output, captured for log file output.
-    /// </summary>
-    public string? Stdout { get; init; }
-
-    /// <summary>
-    /// The sample's stderr output, captured for log file output.
-    /// </summary>
-    public string? Stderr { get; init; }
-
-    /// <summary>
-    /// Per-sample log lines, buffered during parallel execution
-    /// and written sequentially to the log file.
-    /// </summary>
-    public List<string> LogLines { get; init; } = [];
-}
@@ -1,124 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-namespace VerifySamples;
-
-/// <summary>
-/// Parsed command-line options for the sample verification tool.
-/// </summary>
-internal sealed class VerifyOptions
-{
-    /// <summary>
-    /// Maximum number of samples to run concurrently.
-    /// </summary>
-    public int MaxParallelism { get; init; } = 8;
-
-    /// <summary>
-    /// Path to write a CSV summary file, or <c>null</c> to skip.
-    /// </summary>
-    public string? CsvFilePath { get; init; }
-
-    /// <summary>
-    /// Path to write a sequential log file, or <c>null</c> to skip.
-    /// </summary>
-    public string? LogFilePath { get; init; }
-
-    /// <summary>
-    /// The filtered list of samples to process.
-    /// </summary>
-    public required IReadOnlyList<SampleDefinition> Samples { get; init; }
-
-    /// <summary>
-    /// All known sample set registries, keyed by category name.
-    /// </summary>
-    private static readonly Dictionary<string, IReadOnlyList<SampleDefinition>> s_sampleSets =
-        new(StringComparer.OrdinalIgnoreCase)
-        {
-            ["01-get-started"] = GetStartedSamples.All,
-            ["02-agents"] = AgentsSamples.All,
-            ["03-workflows"] = WorkflowSamples.All,
-        };
-
-    /// <summary>
-    /// Parses command-line arguments and resolves the sample list.
-    /// Returns <c>null</c> and writes to stderr if the arguments are invalid.
-    /// </summary>
-    public static VerifyOptions? Parse(string[] args)
-    {
-        var argList = args.ToList();
-
-        var categoryFilter = ExtractArg(argList, "--category");
-        var logFilePath = ExtractArg(argList, "--log");
-        var csvFilePath = ExtractArg(argList, "--csv");
-
-        int maxParallelism = 8;
-        var parallelArg = ExtractArg(argList, "--parallel");
-        if (parallelArg is not null && int.TryParse(parallelArg, out var p) && p > 0)
-        {
-            maxParallelism = p;
-        }
-
-        HashSet<string>? nameFilter = null;
-        if (argList.Count > 0)
-        {
-            nameFilter = argList.ToHashSet(StringComparer.OrdinalIgnoreCase);
-        }
-
-        // Build the sample list
-        IReadOnlyList<SampleDefinition> samples;
-        if (categoryFilter is not null)
-        {
-            if (!s_sampleSets.TryGetValue(categoryFilter, out var categoryList))
-            {
-                Console.Error.WriteLine(
-                    $"Unknown category '{categoryFilter}'. Available: {string.Join(", ", s_sampleSets.Keys)}");
-                return null;
-            }
-
-            samples = categoryList;
-        }
-        else
-        {
-            samples = s_sampleSets.Values.SelectMany(s => s).ToList();
-        }
-
-        if (nameFilter is not null)
-        {
-            samples = samples.Where(s => nameFilter.Contains(s.Name)).ToList();
-        }
-
-        if (samples.Count == 0)
-        {
-            var allNames = s_sampleSets.Values.SelectMany(s => s).Select(s => s.Name);
-            Console.Error.WriteLine($"No matching samples found. Available: {string.Join(", ", allNames)}");
-            return null;
-        }
-
-        return new VerifyOptions
-        {
-            MaxParallelism = maxParallelism,
-            LogFilePath = logFilePath,
-            CsvFilePath = csvFilePath,
-            Samples = samples,
-        };
-    }
-
-    private static string? ExtractArg(List<string> list, string flag)
-    {
-        var idx = list.IndexOf(flag);
-        if (idx < 0)
-        {
-            return null;
-        }
-
-        if (idx + 1 >= list.Count)
-        {
-            Console.Error.WriteLine($"Missing value for {flag}.");
-            list.RemoveAt(idx);
-            return null;
-        }
-
-        var value = list[idx + 1];
-        list.RemoveRange(idx, 2);
-        return value;
-    }
-}
@@ -1,525 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-namespace VerifySamples;
-
-/// <summary>
-/// Defines the expected behavior for each sample in 03-workflows.
-/// </summary>
-internal static class WorkflowSamples
-{
-    public static IReadOnlyList<SampleDefinition> All { get; } =
-    [
-        // ───────────────────────────────────────────────────────────────────
-        // _StartHere
-        // ───────────────────────────────────────────────────────────────────
-
-        new SampleDefinition
-        {
-            Name = "Workflow_StartHere_01_Streaming",
-            ProjectPath = "samples/03-workflows/_StartHere/01_Streaming",
-            RequiredEnvironmentVariables = [],
-            IsDeterministic = true,
-            MustContain =
-            [
-                "UppercaseExecutor: HELLO, WORLD!",
-                "ReverseTextExecutor: !DLROW ,OLLEH",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_StartHere_02_AgentsInWorkflows",
-            ProjectPath = "samples/03-workflows/_StartHere/02_AgentsInWorkflows",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            ExpectedOutputDescription =
-            [
-                "The output should show agent responses from a translation workflow.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_StartHere_03_AgentWorkflowPatterns",
-            ProjectPath = "samples/03-workflows/_StartHere/03_AgentWorkflowPatterns",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            Inputs = ["sequential"],
-            InputDelayMs = 3000,
-            ExpectedOutputDescription =
-            [
-                "The output should show a sequential workflow pattern with multiple agents executing tasks in order.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_StartHere_04_MultiModelService",
-            ProjectPath = "samples/03-workflows/_StartHere/04_MultiModelService",
-            RequiredEnvironmentVariables = ["BEDROCK_ACCESS_KEY", "BEDROCK_SECRET_KEY", "ANTHROPIC_API_KEY", "OPENAI_API_KEY"],
-            SkipReason = "Requires multiple external provider API keys (Bedrock, Anthropic, OpenAI).",
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_StartHere_05_SubWorkflows",
-            ProjectPath = "samples/03-workflows/_StartHere/05_SubWorkflows",
-            RequiredEnvironmentVariables = [],
-            IsDeterministic = true,
-            MustContain =
-            [
-                "=== Sub-Workflow Demonstration ===",
-                "Final Output:",
-                "=== Main Workflow Completed ===",
-                "Sample Complete: Workflows can be composed hierarchically using sub-workflows",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_StartHere_06_MixedWorkflowAgentsAndExecutors",
-            ProjectPath = "samples/03-workflows/_StartHere/06_MixedWorkflowAgentsAndExecutors",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            Inputs = ["What is 2 plus 2?"],
-            InputDelayMs = 3000,
-            ExpectedOutputDescription =
-            [
-                "The output should show agents and executors working together to process a user question.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_StartHere_07_WriterCriticWorkflow",
-            ProjectPath = "samples/03-workflows/_StartHere/07_WriterCriticWorkflow",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            MustContain = ["=== Writer-Critic Iteration Workflow ==="],
-            ExpectedOutputDescription =
-            [
-                "The output should show a writer-critic iteration workflow with writer and critic sections.",
-                "The critic should either approve or request revisions.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        // ───────────────────────────────────────────────────────────────────
-        // Agents
-        // ───────────────────────────────────────────────────────────────────
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Agents_CustomAgentExecutors",
-            ProjectPath = "samples/03-workflows/Agents/CustomAgentExecutors",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            ExpectedOutputDescription =
-            [
-                "The output should show custom workflow events including slogan generation and feedback.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Agents_FoundryAgent",
-            ProjectPath = "samples/03-workflows/Agents/FoundryAgent",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-            SkipReason = "Requires Azure AI Foundry project endpoint.",
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Agents_GroupChatToolApproval",
-            ProjectPath = "samples/03-workflows/Agents/GroupChatToolApproval",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            MustContain = ["Starting group chat workflow for software deployment..."],
-            ExpectedOutputDescription =
-            [
-                "The output should show a group chat workflow with QA and DevOps agents for software deployment.",
-                "There should be approval requests for tool calls.",
-                "The workflow should show interaction between QA and DevOps agents toward deployment.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Agents_WorkflowAsAnAgent",
-            ProjectPath = "samples/03-workflows/Agents/WorkflowAsAnAgent",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            Inputs = ["hello", "exit"],
-            InputDelayMs = 5000,
-            ExpectedOutputDescription =
-            [
-                "The output should show a conversational workflow responding to the user's hello message.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        // ───────────────────────────────────────────────────────────────────
-        // Checkpoint
-        // ───────────────────────────────────────────────────────────────────
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Checkpoint_CheckpointAndRehydrate",
-            ProjectPath = "samples/03-workflows/Checkpoint/CheckpointAndRehydrate",
-            RequiredEnvironmentVariables = [],
-            IsDeterministic = true,
-            MustContain =
-            [
-                "Workflow completed with result:",
-                "Number of checkpoints created:",
-                "Hydrating a new workflow instance from the 6th checkpoint.",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Checkpoint_CheckpointAndResume",
-            ProjectPath = "samples/03-workflows/Checkpoint/CheckpointAndResume",
-            RequiredEnvironmentVariables = [],
-            IsDeterministic = true,
-            MustContain =
-            [
-                "Workflow completed with result:",
-                "Number of checkpoints created:",
-                "Restoring from the 6th checkpoint.",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Checkpoint_CheckpointWithHumanInTheLoop",
-            ProjectPath = "samples/03-workflows/Checkpoint/CheckpointWithHumanInTheLoop",
-            RequiredEnvironmentVariables = [],
-            Inputs = ["50", "25", "40", "45", "42", "50", "25", "40", "45", "42"],
-            InputDelayMs = 1000,
-            MustContain = ["found in"],
-            ExpectedOutputDescription =
-            [
-                "The output should show a number guessing game with higher/lower hints that eventually reaches the correct number.",
-                "The output should demonstrate checkpoint save and restore behavior.",
-            ],
-        },
-
-        // ───────────────────────────────────────────────────────────────────
-        // Concurrent
-        // ───────────────────────────────────────────────────────────────────
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Concurrent_Concurrent",
-            ProjectPath = "samples/03-workflows/Concurrent/Concurrent",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            ExpectedOutputDescription =
-            [
-                "The output should show results from concurrent agent processing.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Concurrent_MapReduce",
-            ProjectPath = "samples/03-workflows/Concurrent/MapReduce",
-            RequiredEnvironmentVariables = [],
-            MustContain =
-            [
-                "=== RUNNING WORKFLOW ===",
-            ],
-        },
-
-        // ───────────────────────────────────────────────────────────────────
-        // ConditionalEdges
-        // ───────────────────────────────────────────────────────────────────
-
-        new SampleDefinition
-        {
-            Name = "Workflow_ConditionalEdges_01_EdgeCondition",
-            ProjectPath = "samples/03-workflows/ConditionalEdges/01_EdgeCondition",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            ExpectedOutputDescription =
-            [
-                "The output should show an email being classified as spam or not spam and processed accordingly.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_ConditionalEdges_02_SwitchCase",
-            ProjectPath = "samples/03-workflows/ConditionalEdges/02_SwitchCase",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            ExpectedOutputDescription =
-            [
-                "The output should show an ambiguous email being classified as spam, not spam, or uncertain.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_ConditionalEdges_03_MultiSelection",
-            ProjectPath = "samples/03-workflows/ConditionalEdges/03_MultiSelection",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            ExpectedOutputDescription =
-            [
-                "The output should show an email being classified and potentially routed to multiple handlers.",
-                "The output should not contain error messages or stack traces.",
-            ],
-        },
-
-        // ───────────────────────────────────────────────────────────────────
-        // HumanInTheLoop
-        // ───────────────────────────────────────────────────────────────────
-
-        new SampleDefinition
-        {
-            Name = "Workflow_HumanInTheLoop_Basic",
-            ProjectPath = "samples/03-workflows/HumanInTheLoop/HumanInTheLoopBasic",
-            RequiredEnvironmentVariables = [],
-            Inputs = ["50", "25", "40", "45", "42"],
-            InputDelayMs = 1000,
-            MustContain = ["found in"],
-            ExpectedOutputDescription =
-            [
-                "The output should show a number guessing game with higher/lower hints that eventually reaches the correct number 42.",
-            ],
-        },
-
-        // ───────────────────────────────────────────────────────────────────
-        // Loop
-        // ───────────────────────────────────────────────────────────────────
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Loop",
-            ProjectPath = "samples/03-workflows/Loop",
-            RequiredEnvironmentVariables = [],
-            MustContain = ["Result:"],
-        },
-
-        // ───────────────────────────────────────────────────────────────────
-        // SharedStates
-        // ───────────────────────────────────────────────────────────────────
-
-        new SampleDefinition
-        {
-            Name = "Workflow_SharedStates",
-            ProjectPath = "samples/03-workflows/SharedStates",
-            RequiredEnvironmentVariables = [],
-            IsDeterministic = true,
-            MustContain =
-            [
-                "Total Paragraphs:",
-                "Total Words:",
-            ],
-        },
-
-        // ───────────────────────────────────────────────────────────────────
-        // Visualization
-        // ───────────────────────────────────────────────────────────────────
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Visualization",
-            ProjectPath = "samples/03-workflows/Visualization",
-            RequiredEnvironmentVariables = [],
-            IsDeterministic = true,
-            MustContain =
-            [
-                "Generating workflow visualization...",
-                "Mermaid string:",
-                "DiGraph string:",
-            ],
-        },
-
-        // ───────────────────────────────────────────────────────────────────
-        // Observability
-        // ───────────────────────────────────────────────────────────────────
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Observability_ApplicationInsights",
-            ProjectPath = "samples/03-workflows/Observability/ApplicationInsights",
-            RequiredEnvironmentVariables = ["APPLICATIONINSIGHTS_CONNECTION_STRING"],
-            SkipReason = "Requires Application Insights connection string.",
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Observability_AspireDashboard",
-            ProjectPath = "samples/03-workflows/Observability/AspireDashboard",
-            RequiredEnvironmentVariables = [],
-            SkipReason = "Requires Aspire Dashboard / OTLP endpoint.",
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Observability_WorkflowAsAnAgent",
-            ProjectPath = "samples/03-workflows/Observability/WorkflowAsAnAgent",
-            RequiredEnvironmentVariables = ["AZURE_OPENAI_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_OPENAI_DEPLOYMENT_NAME"],
-            SkipReason = "Interactive console with ReadLine loop; requires OTLP endpoint.",
-        },
-
-        // ───────────────────────────────────────────────────────────────────
-        // Declarative
-        // ───────────────────────────────────────────────────────────────────
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_ConfirmInput",
-            ProjectPath = "samples/03-workflows/Declarative/ConfirmInput",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            Inputs = ["hello", "hello"],
-            InputDelayMs = 8000,
-            ExpectedOutputDescription = ["The output should show a confirmation prompt and a user response."],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_CustomerSupport",
-            ProjectPath = "samples/03-workflows/Declarative/CustomerSupport",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-            Inputs = ["My laptop won't start"],
-            InputDelayMs = 3000,
-            ExpectedOutputDescription = ["The output should show a customer support workflow processing a laptop issue, with agent responses providing troubleshooting or support."],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_DeepResearch",
-            ProjectPath = "samples/03-workflows/Declarative/DeepResearch",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-            SkipReason = "Requires external weather API (wttr.in).",
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_ExecuteCode",
-            ProjectPath = "samples/03-workflows/Declarative/ExecuteCode",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            Inputs = ["What is 12 * 34?"],
-            InputDelayMs = 5000,
-            ExpectedOutputDescription = ["The output should show a declarative workflow executing generated code, processing a math question and producing a result."],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_ExecuteWorkflow",
-            ProjectPath = "samples/03-workflows/Declarative/ExecuteWorkflow",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            SkipReason = "Requires a workflow file path as a CLI argument.",
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_FunctionTools",
-            ProjectPath = "samples/03-workflows/Declarative/FunctionTools",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-            Inputs = ["What are today's specials?", "EXIT"],
-            InputDelayMs = 8000,
-            ExpectedOutputDescription = ["The output should show a workflow calling function tools (e.g. a menu plugin) to answer a question about restaurant specials."],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_GenerateCode",
-            ProjectPath = "samples/03-workflows/Declarative/GenerateCode",
-            IsDeterministic = true,
-            MustContain = ["WORKFLOW: Parsing", "WORKFLOW: Defined"],
-            ExpectedOutputDescription = ["The output should show a YAML workflow being parsed and C# code being generated from it."],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_HostedWorkflow",
-            ProjectPath = "samples/03-workflows/Declarative/HostedWorkflow",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            SkipReason = "Hosts a persistent workflow server that does not exit.",
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_InputArguments",
-            ProjectPath = "samples/03-workflows/Declarative/InputArguments",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-            Inputs = ["I'd like to visit Seattle", "EXIT"],
-            InputDelayMs = 8000,
-            ExpectedOutputDescription = ["The output should show a workflow capturing location input and providing travel-related information about Seattle."],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_InvokeFunctionTool",
-            ProjectPath = "samples/03-workflows/Declarative/InvokeFunctionTool",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-            Inputs = ["What's the soup of the day?", "EXIT"],
-            InputDelayMs = 8000,
-            ExpectedOutputDescription = ["The output should show a workflow invoking a function tool (e.g. a menu plugin) to answer a question about the soup of the day."],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_InvokeMcpTool",
-            ProjectPath = "samples/03-workflows/Declarative/InvokeMcpTool",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-            Inputs = ["Search for .NET tutorials on Microsoft Learn"],
-            InputDelayMs = 3000,
-            ExpectedOutputDescription = ["The output should show a workflow using MCP tools to search Microsoft Learn documentation and provide a summary of results."],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_Marketing",
-            ProjectPath = "samples/03-workflows/Declarative/Marketing",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-            Inputs = ["A smart water bottle that tracks hydration"],
-            InputDelayMs = 3000,
-            ExpectedOutputDescription = ["The output should show a marketing workflow generating content about a smart water bottle product."],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_StudentTeacher",
-            ProjectPath = "samples/03-workflows/Declarative/StudentTeacher",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-            Inputs = ["What is 18 + 27?"],
-            InputDelayMs = 3000,
-            ExpectedOutputDescription = ["The output should show a student-teacher workflow where a student asks a math question and a teacher provides the answer."],
-        },
-
-        new SampleDefinition
-        {
-            Name = "Workflow_Declarative_ToolApproval",
-            ProjectPath = "samples/03-workflows/Declarative/ToolApproval",
-            RequiredEnvironmentVariables = ["AZURE_AI_PROJECT_ENDPOINT"],
-            OptionalEnvironmentVariables = ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-            Inputs = ["Search for .NET tutorials", "EXIT"],
-            InputDelayMs = 8000,
-            ExpectedOutputDescription = ["The output should show a workflow using an MCP tool with approval to search Microsoft Learn, followed by an exit from the input loop."],
-        },
-    ];
-}
@@ -1,24 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <IsPackable>false</IsPackable>
-    <IsAotCompatible>false</IsAotCompatible>
-    <!-- This is a top-level console app; ConfigureAwait is unnecessary -->
-    <NoWarn>$(NoWarn);CA2007</NoWarn>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" />
-    <PackageReference Include="Azure.Identity" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -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;
                    }
@@ -5,8 +5,8 @@ This sample demonstrates how to create an AIAgent using Anthropic Claude models
 The sample supports three deployment scenarios:

 1. **Anthropic Public API** - Direct connection to Anthropic's public API
-2. **Microsoft Foundry with API Key** - Anthropic models deployed through Microsoft Foundry using API key authentication
-3. **Microsoft Foundry with Azure CLI** - Anthropic models deployed through Microsoft Foundry using Azure CLI credentials
+2. **Azure Foundry with API Key** - Anthropic models deployed through Azure Foundry using API key authentication
+3. **Azure Foundry with Azure CLI** - Anthropic models deployed through Azure Foundry using Azure CLI credentials

 ## Prerequisites

@@ -25,29 +25,29 @@ $env:ANTHROPIC_API_KEY="your-anthropic-api-key"  # Replace with your Anthropic A
 $env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5"  # Optional, defaults to claude-haiku-4-5
 ```

-### For Microsoft Foundry with API Key
+### For Azure Foundry with API Key

- Microsoft Foundry service endpoint and deployment configured
+- Azure Foundry service endpoint and deployment configured
 - Anthropic API key

 Set the following environment variables:

 ```powershell
-$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"  # Replace with your Microsoft Foundry resource name (subdomain before .services.ai.azure.com)
+$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"  # Replace with your Azure Foundry resource name (subdomain before .services.ai.azure.com)
 $env:ANTHROPIC_API_KEY="your-anthropic-api-key"  # Replace with your Anthropic API key
 $env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5"  # Optional, defaults to claude-haiku-4-5
 ```

-### For Microsoft Foundry with Azure CLI
+### For Azure Foundry with Azure CLI

- Microsoft Foundry service endpoint and deployment configured
+- Azure Foundry service endpoint and deployment configured
 - Azure CLI installed and authenticated (for Azure credential authentication)

 Set the following environment variables:

 ```powershell
-$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"  # Replace with your Microsoft Foundry resource name (subdomain before .services.ai.azure.com)
+$env:ANTHROPIC_RESOURCE="your-foundry-resource-name"  # Replace with your Azure Foundry resource name (subdomain before .services.ai.azure.com)
 $env:ANTHROPIC_CHAT_MODEL_NAME="claude-haiku-4-5"  # Optional, defaults to claude-haiku-4-5
 ```

-**Note**: When using Microsoft Foundry with Azure CLI, 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).
+**Note**: When using Azure Foundry with Azure CLI, 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).
@@ -2,7 +2,7 @@

 #pragma warning disable CS0618 // Type or member is obsolete - sample uses deprecated PersistentAgentsClientExtensions

-// This sample shows how to create and use a simple AI agent with Microsoft Foundry Agents as the backend.
+// This sample shows how to create and use a simple AI agent with Azure Foundry Agents as the backend.

 using Azure.AI.Agents.Persistent;
 using Azure.Identity;
@@ -13,14 +13,14 @@ Below is a comparison between the classic and new Foundry Agents approaches:
 Before you begin, ensure you have the following prerequisites:

 - .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
+- 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 Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+**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
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Microsoft Foundry resource endpoint
+$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
 ```
@@ -1,19 +1,18 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to create and use AI agents with Microsoft Foundry Agents as the backend.
+// This sample shows how to create and use a 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;
-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";

 const string JokerName = "JokerAgent";

-// Get a client to create/retrieve/delete server side agents with Microsoft Foundry Agents.
+// 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.
@@ -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}");
@@ -13,14 +13,14 @@ Below is a comparison between the classic and new Foundry Agents approaches:
 Before you begin, ensure you have the following prerequisites:

 - .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
+- 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 Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+**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
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Microsoft Foundry resource endpoint
+$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
 ```
@@ -1,7 +1,7 @@
 // Copyright (c) Microsoft. All rights reserved.

-// This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Microsoft Foundry.
-// You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI or any other model you have deployed in your Microsoft Foundry resource.
+// This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Azure AI Foundry.
+// You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI or any other model you have deployed in your Azure AI Foundry resource.
 // Note: Ensure that you pick a model that suits your needs. For example, if you want to use function calling, ensure that the model you pick supports function calling.

 using System.ClientModel;
@@ -15,7 +15,7 @@ var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? th
 var apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
 var model = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "Phi-4-mini-instruct";

-// Since we are using the OpenAI Client SDK, we need to override the default endpoint to point to Microsoft Foundry.
+// Since we are using the OpenAI Client SDK, we need to override the default endpoint to point to Azure Foundry.
 var clientOptions = new OpenAIClientOptions() { Endpoint = new Uri(endpoint) };

 // Create the OpenAI client with either an API key or Azure CLI credential.
@@ -1,8 +1,8 @@
 ## Overview

-This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Microsoft Foundry.
+This sample shows how to use the OpenAI SDK to create and use a simple AI agent with any model hosted in Azure AI Foundry.

-You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI or any other model you have deployed in Microsoft Foundry.
+You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI or any other model you have deployed in Azure AI Foundry.

 **Note**: Ensure that you pick a model that suits your needs. For example, if you want to use function calling, ensure that the model you pick supports function calling.

@@ -11,19 +11,19 @@ You could use models from Microsoft, OpenAI, DeepSeek, Hugging Face, Meta, xAI o
 Before you begin, ensure you have the following prerequisites:

 - .NET 10 SDK or later
- Microsoft Foundry resource
- A model deployment in your Microsoft Foundry resource. This example defaults to using the `Phi-4-mini-instruct` model,
+- Azure AI Foundry resource
+- A model deployment in your Azure AI Foundry resource. This example defaults to using the `Phi-4-mini-instruct` model,
 so if you want to use a different model, ensure that you set your `AZURE_AI_MODEL_DEPLOYMENT_NAME` environment
 variable to the name of your deployed model.
- An API key or role based authentication to access the Microsoft Foundry resource
+- An API key or role based authentication to access the Azure AI Foundry resource

 See [here](https://learn.microsoft.com/en-us/azure/ai-foundry/quickstarts/get-started-code?tabs=csharp) for more info on setting up these prerequisites

 Set the following environment variables:

 ```powershell
-# Replace with your Microsoft Foundry resource endpoint
-# Ensure that you have the "/openai/v1/" path in the URL, since this is required when using the OpenAI SDK to access Microsoft Foundry models.
+# Replace with your Azure AI Foundry resource endpoint
+# Ensure that you have the "/openai/v1/" path in the URL, since this is required when using the OpenAI SDK to access Azure Foundry models.
 $env:AZURE_OPENAI_ENDPOINT="https://ai-foundry-<myresourcename>.services.ai.azure.com/openai/v1/"

 # Optional, defaults to using Azure CLI for authentication if not provided
@@ -18,7 +18,7 @@ See the README.md for each sample for the prerequisites for that sample.
 |[Creating an AIAgent with Anthropic](./Agent_With_Anthropic/)|This sample demonstrates how to create an AIAgent using Anthropic Claude models as the underlying inference service|
 |[Creating an AIAgent with Foundry Agents using Azure.AI.Agents.Persistent](./Agent_With_AzureAIAgentsPersistent/)|This sample demonstrates how to create a Foundry Persistent agent and expose it as an AIAgent using the Azure.AI.Agents.Persistent SDK|
 |[Creating an AIAgent with Foundry Agents using Azure.AI.Project](./Agent_With_AzureAIProject/)|This sample demonstrates how to create an Foundry Project agent and expose it as an AIAgent using the Azure.AI.Project SDK|
-|[Creating an AIAgent with Foundry Model](./Agent_With_AzureFoundryModel/)|This sample demonstrates how to use any model deployed to Microsoft Foundry to create an AIAgent|
+|[Creating an AIAgent with AzureFoundry Model](./Agent_With_AzureFoundryModel/)|This sample demonstrates how to use any model deployed to Azure Foundry to create an AIAgent|
 |[Creating an AIAgent with Azure OpenAI ChatCompletion](./Agent_With_AzureOpenAIChatCompletion/)|This sample demonstrates how to create an AIAgent using Azure OpenAI ChatCompletion as the underlying inference service|
 |[Creating an AIAgent with Azure OpenAI Responses](./Agent_With_AzureOpenAIResponses/)|This sample demonstrates how to create an AIAgent using Azure OpenAI Responses as the underlying inference service|
 |[Creating an AIAgent with a custom implementation](./Agent_With_CustomImplementation/)|This sample demonstrates how to create an AIAgent with a custom implementation|
@@ -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);
+}
@@ -18,9 +18,9 @@ Before you begin, ensure you have the following prerequisites:

 **Note**: These samples use Anthropic Claude models. For more information, see [Anthropic documentation](https://docs.anthropic.com/).

-## Using Anthropic with Microsoft Foundry
+## Using Anthropic with Azure Foundry

-To use Anthropic with Microsoft Foundry, you can check the sample [AgentProviders/Agent_With_Anthropic](../AgentProviders/Agent_With_Anthropic/README.md) for more details.
+To use Anthropic with Azure Foundry, you can check the sample [AgentProviders/Agent_With_Anthropic](../AgentProviders/Agent_With_Anthropic/README.md) for more details.

 ## Samples

@@ -1,17 +1,16 @@
 // Copyright (c) Microsoft. All rights reserved.

 // This sample shows how to use the FoundryMemoryProvider to persist and recall memories for an agent.
-// The sample stores conversation messages in a Microsoft Foundry memory store and retrieves relevant
+// The sample stores conversation messages in an Azure AI Foundry memory store and retrieves relevant
 // memories for subsequent invocations, even across new sessions.
 //
-// Note: Memory extraction in Microsoft Foundry is asynchronous and takes time. This sample demonstrates
+// Note: Memory extraction in Azure AI Foundry is asynchronous and takes time. This sample demonstrates
 // a simple polling approach to wait for memory updates to complete before querying.

 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]
    });

@@ -62,7 +54,7 @@ await memoryProvider.EnsureStoredMemoriesDeletedAsync(session);
 Console.WriteLine(await agent.RunAsync("Hi there! My name is Taylor and I'm planning a hiking trip to Patagonia in November.", session));
 Console.WriteLine(await agent.RunAsync("I'm travelling with my sister and we love finding scenic viewpoints.", session));

-// Memory extraction in Microsoft Foundry is asynchronous and takes time to process.
+// Memory extraction in Azure AI Foundry is asynchronous and takes time to process.
 // WhenUpdatesCompletedAsync polls all pending updates and waits for them to complete.
 Console.WriteLine("\nWaiting for Foundry Memory to process updates...");
 await memoryProvider.WhenUpdatesCompletedAsync();
@@ -1,6 +1,6 @@
-# Agent with Memory Using Microsoft Foundry
+# Agent with Memory Using Azure AI Foundry

-This sample demonstrates how to create and run an agent that uses Microsoft Foundry's managed memory service to extract and retrieve individual memories across sessions.
+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 across sessions.

 ## Features Demonstrated

@@ -13,7 +13,7 @@ This sample demonstrates how to create and run an agent that uses Microsoft Foun

 ## Prerequisites

-1. Azure subscription with Microsoft Foundry project
+1. Azure subscription with Azure AI Foundry project
 2. Azure OpenAI resource with a chat model deployment (e.g., gpt-4o-mini) and an embedding model deployment (e.g., text-embedding-ada-002)
 3. .NET 10.0 SDK
 4. Azure CLI logged in (`az login`)
@@ -21,7 +21,7 @@ This sample demonstrates how to create and run an agent that uses Microsoft Foun
 ## Environment Variables

 ```bash
-# Microsoft Foundry project endpoint and memory store name
+# Azure AI Foundry project endpoint and memory store name
 export AZURE_AI_PROJECT_ENDPOINT="https://your-account.services.ai.azure.com/api/projects/your-project"
 export AZURE_AI_MEMORY_STORE_ID="my_memory_store"

@@ -48,10 +48,10 @@ The agent will:

 ## Key Differences from Mem0

-| Aspect | Mem0 | Microsoft Foundry Memory |
+| Aspect | Mem0 | Azure AI Foundry Memory |
 |--------|------|------------------------|
 | Authentication | API Key | Azure Identity (DefaultAzureCredential) |
 | Scope | ApplicationId, UserId, AgentId, ThreadId | Single `Scope` string |
 | Memory Types | Single memory store | User Profile + Chat Summary |
-| Hosting | Mem0 cloud or self-hosted | Microsoft Foundry managed service |
+| Hosting | Mem0 cloud or self-hosted | Azure AI Foundry managed service |
 | Store Creation | N/A (automatic) | Explicit via `EnsureMemoryStoreCreatedAsync` |
@@ -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.

@@ -7,7 +7,7 @@ These samples show how to create an agent with the Agent Framework that uses Mem
 |[Chat History memory](./AgentWithMemory_Step01_ChatHistoryMemory/)|This sample demonstrates how to enable an agent to remember messages from previous conversations.|
 |[Memory with MemoryStore](./AgentWithMemory_Step02_MemoryUsingMem0/)|This sample demonstrates how to create and run an agent that uses the Mem0 service to extract and retrieve individual memories.|
 |[Custom Memory Implementation](../../01-get-started/04_memory/)|This sample demonstrates how to create a custom memory component and attach it to an agent.|
-|[Memory with Microsoft Foundry](./AgentWithMemory_Step04_MemoryUsingFoundry/)|This sample demonstrates how to create and run an agent that uses Microsoft Foundry's managed memory service to extract and retrieve individual memories.|
+|[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 Microsoft 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.
@@ -13,7 +13,7 @@ This sample uses Qdrant for the vector store, but this can easily be swapped out
 - User has the `Cognitive Services OpenAI Contributor` role for the Azure OpenAI resource.
 - An existing Qdrant instance. You can use a managed service or run a local instance using Docker, but the sample assumes the instance is running locally.

-**Note**: These samples use Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).
+**Note**: These samples use Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).

 **Note**: These samples use Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource and have the `Cognitive Services OpenAI Contributor` role. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).

@@ -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();

@@ -1,54 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-    <ManagePackageVersionsCentrally>false</ManagePackageVersionsCentrally>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Remove="Microsoft.CodeAnalysis.NetAnalyzers" />
-    <PackageReference Remove="Microsoft.VisualStudio.Threading.Analyzers" />
-    <PackageReference Remove="xunit.analyzers" />
-    <PackageReference Remove="Moq.Analyzers" />
-    <PackageReference Remove="Roslynator.Analyzers" />
-    <PackageReference Remove="Roslynator.CodeAnalysis.Analyzers" />
-    <PackageReference Remove="Roslynator.Formatting.Analyzers" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.AI.OpenAI" Version="2.9.0-beta.1" />
-    <PackageReference Include="Azure.Identity" Version="1.19.0" />
-    <PackageReference Include="Microsoft.Agents.AI.OpenAI" Version="1.0.0-rc4" />
-    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="10.4.0" />
-    <PackageReference Include="Neo4j.AgentFramework.GraphRAG" Version="0.1.0-preview.2" />
-    <PackageReference Include="Neo4j.Driver" Version="5.28.0" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Microsoft.CodeAnalysis.NetAnalyzers" Version="10.0.100">
-      <PrivateAssets>all</PrivateAssets>
-      <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
-    </PackageReference>
-    <PackageReference Include="Microsoft.VisualStudio.Threading.Analyzers" Version="17.14.15">
-      <PrivateAssets>all</PrivateAssets>
-      <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
-    </PackageReference>
-    <PackageReference Include="Roslynator.Analyzers" Version="4.14.1">
-      <PrivateAssets>all</PrivateAssets>
-      <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
-    </PackageReference>
-    <PackageReference Include="Roslynator.CodeAnalysis.Analyzers" Version="4.14.1">
-      <PrivateAssets>all</PrivateAssets>
-      <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
-    </PackageReference>
-    <PackageReference Include="Roslynator.Formatting.Analyzers" Version="4.14.1">
-      <PrivateAssets>all</PrivateAssets>
-      <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
-    </PackageReference>
-  </ItemGroup>
-
-</Project>
@@ -1,77 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-using Azure.AI.OpenAI;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-using Neo4j.AgentFramework.GraphRAG;
-using Neo4j.Driver;
-
-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 neo4jUri = Environment.GetEnvironmentVariable("NEO4J_URI") ?? throw new InvalidOperationException("NEO4J_URI is not set.");
-var neo4jUsername = Environment.GetEnvironmentVariable("NEO4J_USERNAME") ?? "neo4j";
-var neo4jPassword = Environment.GetEnvironmentVariable("NEO4J_PASSWORD") ?? throw new InvalidOperationException("NEO4J_PASSWORD is not set.");
-var fulltextIndex = Environment.GetEnvironmentVariable("NEO4J_FULLTEXT_INDEX_NAME") ?? "search_chunks";
-
-const string RetrievalQuery = """
-    MATCH (node)-[:FROM_DOCUMENT]->(doc:Document)<-[:FILED]-(company:Company)
-    OPTIONAL MATCH (company)-[:FACES_RISK]->(risk:RiskFactor)
-    WITH node, score, company, doc, collect(DISTINCT risk.name)[0..5] AS risks
-    OPTIONAL MATCH (company)-[:MENTIONS]->(product:Product)
-    WITH node, score, company, doc, risks, collect(DISTINCT product.name)[0..5] AS products
-    RETURN
-        node.text AS text,
-        score,
-        company.name AS company,
-        company.ticker AS ticker,
-        doc.title AS title,
-        risks,
-        products
-    ORDER BY score DESC
-    """;
-
-await using var driver = GraphDatabase.Driver(new Uri(neo4jUri), AuthTokens.Basic(neo4jUsername, neo4jPassword));
-await driver.VerifyConnectivityAsync();
-
-await using var provider = new Neo4jContextProvider(
-    driver,
-    new Neo4jContextProviderOptions
-    {
-        IndexName = fulltextIndex,
-        IndexType = IndexType.Fulltext,
-        RetrievalQuery = RetrievalQuery,
-        TopK = 5,
-        ContextPrompt = "Use the retrieved Neo4j graph context to answer accurately and call out when context is missing."
-    });
-
-// 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 AzureOpenAIClient(
-    new Uri(endpoint),
-    new DefaultAzureCredential())
-    .GetChatClient(deploymentName)
-    .AsIChatClient()
-    .AsAIAgent(new ChatClientAgentOptions
-    {
-        ChatOptions = new()
-        {
-            Instructions = "You are a helpful assistant that answers questions using Neo4j graph context."
-        },
-        AIContextProviders = [provider]
-    });
-
-AgentSession session = await agent.CreateSessionAsync();
-
-foreach (var question in new[]
-{
-    "What products does Microsoft offer?",
-    "What risks does Apple face?",
-    "Tell me about NVIDIA's AI business and risk factors."
-})
-{
-    Console.WriteLine($">> {question}\n");
-    Console.WriteLine(await agent.RunAsync(question, session));
-    Console.WriteLine();
-}
@@ -1,32 +0,0 @@
-# Agent Framework Retrieval Augmented Generation (RAG) with Neo4j GraphRAG
-
-This sample demonstrates how to create and run an agent that uses the [Neo4j GraphRAG context provider](https://github.com/neo4j-labs/neo4j-maf-provider) with Microsoft Agent Framework for .NET.
-
-The sample uses a Neo4j fulltext index for retrieval and a Cypher `RetrievalQuery` to enrich results with related companies, products, and risk factors.
-
-## Prerequisites
-
- .NET 10 SDK or later
- Azure OpenAI endpoint and chat deployment
- Azure CLI installed and authenticated
- A Neo4j database with chunked documents and a fulltext index such as `search_chunks`
-
-## Environment variables
-
-```powershell
-$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
-$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"
-$env:NEO4J_URI="neo4j+s://your-instance.databases.neo4j.io"
-$env:NEO4J_USERNAME="neo4j"
-$env:NEO4J_PASSWORD="your-password"
-$env:NEO4J_FULLTEXT_INDEX_NAME="search_chunks"
-```
-
-## Build and run
-
-```powershell
-dotnet build
-dotnet run --framework net10.0 --no-build
-```
-
-The sample issues a few questions against the graph-backed retrieval provider and prints the responses to the console.
@@ -8,4 +8,3 @@ These samples show how to create an agent with the Agent Framework that uses Ret
 |[RAG with Vector Store and custom schema](./AgentWithRAG_Step02_CustomVectorStoreRAG/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with a vector store. It also uses a custom schema for the documents stored in the vector store.|
 |[RAG with custom RAG data source](./AgentWithRAG_Step03_CustomRAGDataSource/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with a custom RAG data source.|
 |[RAG with Foundry VectorStore service](./AgentWithRAG_Step04_FoundryServiceRAG/)|This sample demonstrates how to create and run an agent that uses Retrieval Augmented Generation (RAG) with the Foundry VectorStore service.|
-|[RAG with Neo4j GraphRAG](./AgentWithRAG_Step05_Neo4jGraphRAG/)|This sample demonstrates how to create and run an agent that uses a Neo4j-backed GraphRAG context provider with graph-enriched retrieval.|
@@ -18,7 +18,7 @@ Before you begin, ensure you have the following prerequisites:
 - Azure CLI installed and authenticated (for Azure credential authentication)
 - User has the `Cognitive Services OpenAI Contributor` role for the Azure OpenAI resource

-**Note**: This sample uses Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).
+**Note**: This sample uses Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).

 **Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource and have the `Cognitive Services OpenAI Contributor` role. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).

@@ -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.
@@ -20,8 +20,8 @@ To use the [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector)
   MCP Inspector is up and running at http://127.0.0.1:6274
   ```
 1. Open a web browser and navigate to the URL displayed in the terminal. If not opened automatically, this will open the MCP Inspector interface.
-1. In the MCP Inspector interface, add the following environment variables to allow your MCP server to access Microsoft Foundry Project to create and run the agent:
-    - AZURE_AI_PROJECT_ENDPOINT = https://your-resource.openai.azure.com/ # Replace with your Microsoft Foundry Project endpoint
+1. In the MCP Inspector interface, add the following environment variables to allow your MCP server to access Azure AI Foundry Project to create and run the agent:
+    - AZURE_AI_PROJECT_ENDPOINT = https://your-resource.openai.azure.com/ # Replace with your Azure AI Foundry Project endpoint
    - AZURE_AI_MODEL_DEPLOYMENT_NAME = gpt-4o-mini # Replace with your model deployment name
 1. Find and click the `Connect` button in the MCP Inspector interface to connect to the MCP server.
 1. As soon as the connection is established, open the `Tools` tab in the MCP Inspector interface and select the `Joker` tool from the list.
@@ -13,7 +13,7 @@ using Azure.Identity;
 using Microsoft.Agents.AI;
 using Microsoft.Extensions.AI;

-// Get Microsoft Foundry configuration from environment variables
+// Get Azure AI Foundry configuration from environment variables
 var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
 var deploymentName = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o";

@@ -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();
-}
@@ -3,7 +3,7 @@
 // This sample shows how to use a chat history reducer to keep the context within model size limits.
 // Any implementation of Microsoft.Extensions.AI.IChatReducer can be used to customize how the chat history is reduced.
 // NOTE: this feature is only supported where the chat history is stored locally, such as with OpenAI Chat Completion.
-// Where the chat history is stored server side, such as with Microsoft Foundry Agents, the service must manage the chat history size.
+// Where the chat history is stored server side, such as with Azure Foundry Agents, the service must manage the chat history size.

 using Azure.AI.OpenAI;
 using Azure.Identity;
@@ -2,7 +2,7 @@

 #pragma warning disable CS0618 // Type or member is obsolete - sample uses deprecated PersistentAgentsClientExtensions

-// This sample shows how to create a Microsoft Foundry Agent with the Deep Research Tool.
+// This sample shows how to create an Azure AI Foundry Agent with the Deep Research Tool.

 using Azure.AI.Agents.Persistent;
 using Azure.Identity;
@@ -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.
@@ -11,10 +11,10 @@ Key features:

 Before running this sample, ensure you have:

-1. A Microsoft Foundry project set up
+1. An Azure AI Foundry project set up
 2. A deep research model deployment (e.g., o3-deep-research)
 3. A model deployment (e.g., gpt-4o)
-4. A Bing Connection configured in your Microsoft Foundry project
+4. A Bing Connection configured in your Azure AI Foundry project
 5. Azure CLI installed and authenticated

 **Important**: Please visit the following documentation for detailed setup instructions:
@@ -23,24 +23,22 @@ 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 Microsoft Foundry portal under **Management > Connected resources**, or retrieve it programmatically via the connections API (`.id` property).
-
 ## Environment Variables

 Set the following environment variables:

 ```powershell
-# Replace with your Microsoft Foundry project endpoint
+# 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)
 ```

@@ -18,7 +18,7 @@ Before you begin, ensure you have the following prerequisites:
 - Azure CLI installed and authenticated (for Azure credential authentication)
 - User has the `Cognitive Services OpenAI Contributor` role for the Azure OpenAI resource.

-**Note**: These samples use Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).
+**Note**: These samples use Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).

 **Note**: These samples use Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource and have the `Cognitive Services OpenAI Contributor` role. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).

@@ -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 Microsoft 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
-```
--- 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