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
262 changed files with 4208 additions and 4556 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
@@ -67,13 +67,11 @@ jobs:
      # Azure AI configuration
      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
      # Azure OpenAI configuration
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
      AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
-      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME }}
+      AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
      # OpenAI configuration
      OPENAI_API_KEY: ${{ secrets.OPENAI__APIKEY }}
      OPENAI_CHAT_MODEL_ID: ${{ vars.OPENAI__CHATMODELID }}
@@ -99,8 +97,6 @@ jobs:

      - name: Create .env for samples
        run: |
-          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
-          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
          echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
@@ -129,7 +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: ${{ vars.OPENAI__RESPONSESMODELID }}
    defaults:
@@ -149,7 +144,6 @@ jobs:
      - name: Create .env for samples
        run: |
          echo "OPENAI_API_KEY=$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

@@ -164,8 +158,8 @@ jobs:
          name: validation-report-02-agents-openai
          path: python/samples/sample_validation/reports/

-  validate-02-agents-azure:
-    name: Validate 02-agents/providers/azure
+  validate-02-agents-azure-openai:
+    name: Validate 02-agents/providers/azure_openai
    runs-on: ubuntu-latest
    environment: integration
    env:
@@ -196,13 +190,93 @@ jobs:

      - name: Run sample validation
        run: |
-          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure --save-report --report-name 02-agents-azure
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure_openai --save-report --report-name 02-agents-azure-openai

      - name: Upload validation report
        uses: actions/upload-artifact@v7
        if: always()
        with:
-          name: validation-report-02-agents-azure
+          name: validation-report-02-agents-azure-openai
+          path: python/samples/sample_validation/reports/
+
+  validate-02-agents-azure-ai:
+    name: Validate 02-agents/providers/azure_ai
+    runs-on: ubuntu-latest
+    environment: integration
+    env:
+      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
+      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+      AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
+      AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__EMBEDDINGDEPLOYMENTNAME }}
+      BING_CONNECTION_ID: ${{ secrets.BING_CONNECTION_ID }}
+    defaults:
+      run:
+        working-directory: python
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Setup environment
+        uses: ./.github/actions/sample-validation-setup
+        with:
+          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
+          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+          os: ${{ runner.os }}
+
+      - name: Create .env for samples
+        run: |
+          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
+          echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME=$AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME" >> .env
+          echo "AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME=$AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME" >> .env
+          echo "BING_CONNECTION_ID=$BING_CONNECTION_ID" >> .env
+
+      - name: Run sample validation
+        run: |
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure_ai --save-report --report-name 02-agents-azure-ai
+
+      - name: Upload validation report
+        uses: actions/upload-artifact@v7
+        if: always()
+        with:
+          name: validation-report-02-agents-azure-ai
+          path: python/samples/sample_validation/reports/
+
+  validate-02-agents-azure-ai-agent:
+    name: Validate 02-agents/providers/azure_ai_agent
+    runs-on: ubuntu-latest
+    environment: integration
+    env:
+      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
+      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
+    defaults:
+      run:
+        working-directory: python
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Setup environment
+        uses: ./.github/actions/sample-validation-setup
+        with:
+          azure-client-id: ${{ secrets.AZURE_CLIENT_ID }}
+          azure-tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+          azure-subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+          os: ${{ runner.os }}
+
+      - name: Create .env for samples
+        run: |
+          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
+          echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
+
+      - name: Run sample validation
+        run: |
+          cd scripts && uv run python -m sample_validation --subdir 02-agents/providers/azure_ai_agent --save-report --report-name 02-agents-azure-ai-agent
+
+      - name: Upload validation report
+        uses: actions/upload-artifact@v7
+        if: always()
+        with:
+          name: validation-report-02-agents-azure-ai-agent
          path: python/samples/sample_validation/reports/

  validate-02-agents-anthropic:
@@ -335,13 +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
+  validate-02-agents-foundry-local:
+    name: Validate 02-agents/providers/foundry_local
+    if: false # Temporarily disabled - requires local Foundry setup
    runs-on: ubuntu-latest
    environment: integration
-    env:
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
    defaults:
      run:
        working-directory: python
@@ -356,20 +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
-
      - 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:
@@ -449,8 +516,6 @@ jobs:
    environment: integration
    env:
      # Azure AI configuration
-      FOUNDRY_PROJECT_ENDPOINT: ${{ vars.FOUNDRY_PROJECT_ENDPOINT }}
-      FOUNDRY_MODEL: ${{ vars.FOUNDRY_MODEL }}
      AZURE_AI_PROJECT_ENDPOINT: ${{ vars.AZURE_AI_PROJECT_ENDPOINT }}
      AZURE_AI_MODEL_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      # Azure OpenAI configuration
@@ -473,8 +538,6 @@ jobs:

      - name: Create .env for samples
        run: |
-          echo "FOUNDRY_PROJECT_ENDPOINT=$FOUNDRY_PROJECT_ENDPOINT" >> .env
-          echo "FOUNDRY_MODEL=$FOUNDRY_MODEL" >> .env
          echo "AZURE_AI_PROJECT_ENDPOINT=$AZURE_AI_PROJECT_ENDPOINT" >> .env
          echo "AZURE_AI_MODEL_DEPLOYMENT_NAME=$AZURE_AI_MODEL_DEPLOYMENT_NAME" >> .env
          echo "AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT" >> .env
@@ -696,12 +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
@@ -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.
@@ -1,4 +1,4 @@
-<Solution>
+<Solution>
  <Configurations>
    <BuildType Name="Debug" />
    <BuildType Name="Publish" />
@@ -121,34 +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" />
@@ -171,6 +143,35 @@
    <Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step03_CustomRAGDataSource/AgentWithRAG_Step03_CustomRAGDataSource.csproj" />
    <Project Path="samples/02-agents/AgentWithRAG/AgentWithRAG_Step04_FoundryServiceRAG/AgentWithRAG_Step04_FoundryServiceRAG.csproj" />
  </Folder>
+  <Folder Name="/Samples/02-agents/FoundryAgents/">
+    <File Path="samples/02-agents/FoundryAgents/README.md" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step01_RedTeaming/FoundryAgents_Evaluations_Step01_RedTeaming.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step02_SelfReflection/FoundryAgents_Evaluations_Step02_SelfReflection.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step01.1_Basics/FoundryAgents_Step01.1_Basics.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step01.2_Running/FoundryAgents_Step01.2_Running.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step02_MultiturnConversation/FoundryAgents_Step02_MultiturnConversation.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step03_UsingFunctionTools/FoundryAgents_Step03_UsingFunctionTools.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step04_UsingFunctionToolsWithApprovals/FoundryAgents_Step04_UsingFunctionToolsWithApprovals.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step05_StructuredOutput/FoundryAgents_Step05_StructuredOutput.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step06_PersistedConversations/FoundryAgents_Step06_PersistedConversations.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step07_Observability/FoundryAgents_Step07_Observability.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step08_DependencyInjection/FoundryAgents_Step08_DependencyInjection.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step09_UsingMcpClientAsTools/FoundryAgents_Step09_UsingMcpClientAsTools.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step10_UsingImages/FoundryAgents_Step10_UsingImages.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step11_AsFunctionTool/FoundryAgents_Step11_AsFunctionTool.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step12_Middleware/FoundryAgents_Step12_Middleware.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step13_Plugins/FoundryAgents_Step13_Plugins.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step14_CodeInterpreter/FoundryAgents_Step14_CodeInterpreter.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step15_ComputerUse/FoundryAgents_Step15_ComputerUse.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step16_FileSearch/FoundryAgents_Step16_FileSearch.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step17_OpenAPITools/FoundryAgents_Step17_OpenAPITools.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step18_BingCustomSearch/FoundryAgents_Step18_BingCustomSearch.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step19_SharePoint/FoundryAgents_Step19_SharePoint.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step20_MicrosoftFabric/FoundryAgents_Step20_MicrosoftFabric.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step21_WebSearch/FoundryAgents_Step21_WebSearch.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step22_MemorySearch/FoundryAgents_Step22_MemorySearch.csproj" />
+    <Project Path="samples/02-agents/FoundryAgents/FoundryAgents_Step23_LocalMCP/FoundryAgents_Step23_LocalMCP.csproj" />
+  </Folder>
  <Folder Name="/Samples/02-agents/ModelContextProtocol/">
    <File Path="samples/02-agents/ModelContextProtocol/README.md" />
    <Project Path="samples/02-agents/ModelContextProtocol/Agent_MCP_Server/Agent_MCP_Server.csproj" />
@@ -317,8 +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" />
@@ -70,7 +70,7 @@ while ((input = Console.ReadLine()) != null && !input.Equals("exit", StringCompa

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

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

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

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

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

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

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

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

 // The AIAgent version can be accessed via the GetService method.
 Console.WriteLine($"Latest agent version id: {latestAgentVersion.Id}");
@@ -5,13 +5,20 @@
 using Anthropic;
 using Anthropic.Core;
 using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;

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

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

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

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

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

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

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

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

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

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

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

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

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

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

-// Once you have the agent, you can invoke it like any other AIAgent.
-Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
+UserChatMessage chatMessage = new("Tell me a joke about a pirate.");
+
+// Invoke the agent and output the text result.
+ChatCompletion chatCompletion = await agent.RunAsync([chatMessage]);
+Console.WriteLine(chatCompletion.Content.Last().Text);
+
+// Invoke the agent with streaming support.
+AsyncCollectionResult<StreamingChatCompletionUpdate> completionUpdates = agent.RunStreamingAsync([chatMessage]);
+await foreach (StreamingChatCompletionUpdate completionUpdate in completionUpdates)
+{
+    if (completionUpdate.ContentUpdate.Count > 0)
+    {
+        Console.WriteLine(completionUpdate.ContentUpdate[0].Text);
+    }
+}
@@ -4,13 +4,11 @@

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

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

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

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

 AgentSession session = await agent.CreateSessionAsync();

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

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

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

 // Convert the agent to an AIFunction and then to an MCP tool.
 // The agent name and description will be used as the mcp tool name and description.
@@ -189,9 +189,9 @@ async Task<AgentResponse> PIIMiddleware(IEnumerable<ChatMessage> messages, Agent
        // Regex patterns for PII detection (simplified for demonstration)
        Regex[] piiPatterns =
        [
-            MyRegex(), // Phone number (e.g., 123-456-7890)
-            EmailRegex(), // Email address
-            FullNameRegex() // Full name (e.g., John Doe)
+            new(@"\b\d{3}-\d{3}-\d{4}\b", RegexOptions.Compiled), // Phone number (e.g., 123-456-7890)
+            new(@"\b[\w\.-]+@[\w\.-]+\.\w+\b", RegexOptions.Compiled), // Email address
+            new(@"\b[A-Z][a-z]+\s[A-Z][a-z]+\b", RegexOptions.Compiled) // Full name (e.g., John Doe)
        ];

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

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

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

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

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

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

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

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

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

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

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

        private static void SetTodoItems(AgentSession? session, List<string> items)
            => session?.StateBag.SetValue(nameof(TodoListAIContextProvider), items);
@@ -1,36 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to create, use, and clean up a FoundryAgent backed by a server-side
-// versioned agent in Azure AI Foundry. It demonstrates the full lifecycle:
-// create agent version -> wrap as FoundryAgent -> run -> delete.
-
-using Azure.AI.Projects;
-using Azure.AI.Projects.Agents;
-using Azure.Identity;
-using Microsoft.Agents.AI.AzureAI;
-
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-const string JokerName = "JokerAgent";
-
-// Create the AIProjectClient to manage server-side agents.
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
-
-// Create a server-side agent version using the native SDK.
-AgentVersion agentVersion = await aiProjectClient.Agents.CreateAgentVersionAsync(
-    JokerName,
-    new AgentVersionCreationOptions(
-        new PromptAgentDefinition(model: deploymentName)
-        {
-            Instructions = "You are good at telling jokes.",
-        }));
-
-// Wrap the agent version as a FoundryAgent using the AsAIAgent extension.
-FoundryAgent agent = aiProjectClient.AsAIAgent(agentVersion);
-
-// Once you have the agent, you can invoke it like any other AIAgent.
-Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
-
-// Cleanup: deletes the agent and all its versions.
-await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
@@ -1,23 +0,0 @@
-# Agent Step 00 - FoundryAgent Lifecycle
-
-This sample demonstrates the full lifecycle of a `FoundryAgent` backed by a server-side versioned agent in Microsoft Foundry: create → run → delete.
-
-## Prerequisites
-
- A Microsoft Foundry project endpoint
- A model deployment name (defaults to `gpt-4o-mini`)
- Azure CLI installed and authenticated
-
-## Environment Variables
-
-| Variable | Description | Required |
-| --- | --- | --- |
-| `AZURE_AI_PROJECT_ENDPOINT` | Microsoft Foundry project endpoint | Yes |
-| `AZURE_AI_MODEL_DEPLOYMENT_NAME` | Model deployment name | No (defaults to `gpt-4o-mini`) |
-
-## Running the sample
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step00_FoundryAgentLifecycle
-```
@@ -1,15 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,20 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to create and run a basic agent with AIProjectClient.AsAIAgent(...).
-
-using Azure.AI.Projects;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent =
-    new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
-    .AsAIAgent(model: deploymentName, instructions: "You are good at telling jokes.", name: "JokerAgent");
-
-// Once you have the agent, you can invoke it like any other AIAgent.
-Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
@@ -1,55 +0,0 @@
-# Creating and Running a Basic Agent with the Responses API
-
-This sample demonstrates how to create and run a basic AI agent using the `ChatClientAgent`, which uses the Microsoft Foundry Responses API directly without creating server-side agent definitions.
-
-## What this sample demonstrates
-
- Creating a `ChatClientAgent` with instructions and a model
- Running a simple single-turn conversation
- No server-side agent creation or cleanup required
-
-## Prerequisites
-
-Before you begin, ensure you have the following prerequisites:
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
-
-**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-Navigate to the AgentsWithFoundry sample directory and run:
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step01_Basics
-```
-
-## Alternative: Composable approach
-
-You can also create the same agent by composing the underlying `IChatClient` directly. This gives you full control over the chat client pipeline:
-
-```csharp
-using Azure.AI.Projects;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
-
-AIAgent agent = new ChatClientAgent(
-    chatClient: aiProjectClient.GetProjectOpenAIClient().GetProjectResponsesClient().AsIChatClient(deploymentName),
-    instructions: "You are good at telling jokes.",
-    name: "JokerAgent");
-```
-
-This approach is useful when you need to customize the chat client pipeline or swap providers (e.g., Anthropic, OpenAI) while keeping the same agent code.
@@ -1,26 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to create a multi-turn conversation agent using sessions.
-// Context is preserved across multiple runs via response ID chaining in the session.
-
-using Azure.AI.Projects;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
-    .AsAIAgent(deploymentName, instructions: "You are good at telling jokes.", name: "JokerAgent");
-
-// Create a session to maintain context across multiple runs.
-AgentSession session = await agent.CreateSessionAsync();
-
-// First turn
-Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", session));
-
-// Second turn — the agent remembers the first turn via the session.
-Console.WriteLine(await agent.RunAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", session));
@@ -1,36 +0,0 @@
-# Multi-turn Conversation
-
-This sample demonstrates how to implement multi-turn conversations where context is preserved across multiple agent runs using sessions and response ID chaining.
-
-## What this sample demonstrates
-
- Creating an agent with instructions
- Using sessions to maintain conversation context across multiple runs
- Response ID chaining for multi-turn conversations
- No server-side conversation creation required
-
-## Prerequisites
-
-Before you begin, ensure you have the following prerequisites:
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
-
-**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-Navigate to the AgentsWithFoundry sample directory and run:
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step02.1_MultiturnConversation
-```
@@ -1,15 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,34 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to use server-side conversations with a FoundryAgent.
-// Server-side conversations persist on the Foundry service and are visible in the Foundry Project UI.
-// Use this when you need conversation history to be stored and accessible server-side.
-
-using Azure.AI.Projects;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.AzureAI;
-
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-FoundryAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
-    .AsAIAgent(deploymentName, instructions: "You are good at telling jokes.", name: "JokerAgent");
-
-// CreateConversationSessionAsync creates a server-side ProjectConversation
-// that persists on the Foundry service and is visible in the Foundry Project UI.
-AgentSession session = await agent.CreateConversationSessionAsync();
-
-Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", session));
-Console.WriteLine(await agent.RunAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", session));
-
-// Streaming with server-side conversation context.
-await foreach (AgentResponseUpdate update in agent.RunStreamingAsync("Tell me another joke, but about a ninja this time.", session))
-{
-    Console.Write(update);
-}
-
-Console.WriteLine();
@@ -1,36 +0,0 @@
-# Multi-turn Conversation with Server-Side Conversations
-
-This sample demonstrates how to use server-side conversations with a `FoundryAgent`. Server-side conversations persist on the Foundry service and are visible in the Foundry Project UI, making them ideal when you need conversation history to be stored and accessible server-side.
-
-## What this sample demonstrates
-
- Creating a `FoundryAgent` with instructions
- Using `CreateConversationSessionAsync` to create a server-side `ProjectConversation`
- Multi-turn conversations with both text and streaming output
- Server-side conversation persistence visible in the Foundry Project UI
-
-## Prerequisites
-
-Before you begin, ensure you have the following prerequisites:
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
-
-**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-Navigate to the AgentsWithFoundry sample directory and run:
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step02.2_MultiturnWithServerConversations
-```
@@ -1,15 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,41 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample demonstrates how to use function tools.
-
-using System.ComponentModel;
-using Azure.AI.Projects;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-
-[Description("Get the weather for a given location.")]
-static string GetWeather([Description("The location to get the weather for.")] string location)
-    => $"The weather in {location} is cloudy with a high of 15°C.";
-
-// Define the function tool.
-AITool tool = AIFunctionFactory.Create(GetWeather);
-
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
-
-// Create a AIAgent with function tools.
-AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
-    instructions: "You are a helpful assistant that can get weather information.",
-    name: "WeatherAssistant",
-    tools: [tool]);
-
-// Non-streaming agent interaction with function tools.
-AgentSession session = await agent.CreateSessionAsync();
-Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?", session));
-
-// Streaming agent interaction with function tools.
-session = await agent.CreateSessionAsync();
-await foreach (AgentResponseUpdate update in agent.RunStreamingAsync("What is the weather like in Amsterdam?", session))
-{
-    Console.Write(update);
-}
@@ -1,37 +0,0 @@
-# Using Function Tools with the Responses API
-
-This sample demonstrates how to use function tools with the `ChatClientAgent`, allowing the agent to call custom functions to retrieve information.
-
-## What this sample demonstrates
-
- Creating function tools using `AIFunctionFactory`
- Passing function tools to a `ChatClientAgent`
- Running agents with function tools (text output)
- Running agents with function tools (streaming output)
- No server-side agent creation or cleanup required
-
-## Prerequisites
-
-Before you begin, ensure you have the following prerequisites:
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
-
-**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Microsoft Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-Navigate to the AgentsWithFoundry sample directory and run:
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step03_UsingFunctionTools
-```
@@ -1,15 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,30 +0,0 @@
-# Using Function Tools with Approvals via the Responses API
-
-This sample demonstrates how to use function tools that require human-in-the-loop approval before execution.
-
-## What this sample demonstrates
-
- Creating function tools that require approval using `ApprovalRequiredAIFunction`
- Handling approval requests from the agent
- Passing approval responses back to the agent
- No server-side agent creation or cleanup required
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step04_UsingFunctionToolsWithApprovals
-```
@@ -1,15 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,29 +0,0 @@
-# Structured Output with the Responses API
-
-This sample demonstrates how to configure an agent to produce structured output using JSON schema.
-
-## What this sample demonstrates
-
- Using `RunAsync<T>()` to get typed structured output from the agent
- Deserializing streamed responses into structured types
- No server-side agent creation or cleanup required
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step05_StructuredOutput
-```
@@ -1,15 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,30 +0,0 @@
-# Persisted Conversations with the Responses API
-
-This sample demonstrates how to persist and resume agent conversations using session serialization.
-
-## What this sample demonstrates
-
- Serializing agent sessions to JSON for persistence
- Saving and loading sessions from disk
- Resuming conversations with preserved context
- No server-side agent creation or cleanup required
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step06_PersistedConversations
-```
@@ -1,31 +0,0 @@
-# Observability with the Responses API
-
-This sample demonstrates how to add OpenTelemetry observability to an agent using console and Azure Monitor exporters.
-
-## What this sample demonstrates
-
- Configuring OpenTelemetry tracing with console exporter
- Optional Azure Application Insights integration
- Using `.AsBuilder().UseOpenTelemetry()` to add telemetry to the agent
- No server-side agent creation or cleanup required
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-$env:APPLICATIONINSIGHTS_CONNECTION_STRING="..."  # Optional
-```
-
-## Run the sample
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step07_Observability
-```
@@ -1,83 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to use dependency injection to register a AIAgent and use it from a hosted service.
-
-using Azure.AI.Projects;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.DependencyInjection;
-using Microsoft.Extensions.Hosting;
-using SampleApp;
-
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
-
-AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
-    instructions: "You are good at telling jokes.",
-    name: "JokerAgent");
-
-// Create a host builder that we will register services with and then run.
-HostApplicationBuilder builder = Host.CreateApplicationBuilder(args);
-
-// Add the AI agent to the service collection.
-builder.Services.AddSingleton(agent);
-
-// Add a sample service that will use the agent to respond to user input.
-builder.Services.AddHostedService<SampleService>();
-
-// Build and run the host.
-using IHost host = builder.Build();
-await host.RunAsync().ConfigureAwait(false);
-
-namespace SampleApp
-{
-    /// <summary>
-    /// A sample service that uses an AI agent to respond to user input.
-    /// </summary>
-    internal sealed class SampleService(AIAgent agent, IHostApplicationLifetime appLifetime) : IHostedService
-    {
-        private AgentSession? _session;
-
-        public async Task StartAsync(CancellationToken cancellationToken)
-        {
-            this._session = await agent.CreateSessionAsync(cancellationToken);
-            _ = this.RunAsync(appLifetime.ApplicationStopping);
-        }
-
-        public async Task RunAsync(CancellationToken cancellationToken)
-        {
-            await Task.Delay(100, cancellationToken);
-
-            while (!cancellationToken.IsCancellationRequested)
-            {
-                Console.WriteLine("\nAgent: Ask me to tell you a joke about a specific topic. To exit just press Ctrl+C or enter without any input.\n");
-                Console.Write("> ");
-                string? input = Console.ReadLine();
-
-                if (string.IsNullOrWhiteSpace(input))
-                {
-                    appLifetime.StopApplication();
-                    break;
-                }
-
-                await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(input, this._session, cancellationToken: cancellationToken))
-                {
-                    Console.Write(update);
-                }
-
-                Console.WriteLine();
-            }
-        }
-
-        public Task StopAsync(CancellationToken cancellationToken)
-        {
-            Console.WriteLine("\nShutting down...");
-            return Task.CompletedTask;
-        }
-    }
-}
@@ -1,30 +0,0 @@
-# Dependency Injection with the Responses API
-
-This sample demonstrates how to register a `ChatClientAgent` in a dependency injection container and use it from a hosted service.
-
-## What this sample demonstrates
-
- Registering `ChatClientAgent` as an `AIAgent` in the service collection
- Using the agent from a `IHostedService` with an interactive chat loop
- Streaming responses in a hosted service context
- No server-side agent creation or cleanup required
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step08_DependencyInjection
-```
@@ -1,44 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to use MCP client tools with an agent.
-// It connects to the Microsoft Learn MCP server via HTTP and uses its tools.
-
-using Azure.AI.Projects;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-using ModelContextProtocol.Client;
-
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// Connect to the Microsoft Learn MCP server via HTTP (Streamable HTTP transport).
-Console.WriteLine("Connecting to MCP server at https://learn.microsoft.com/api/mcp ...");
-
-await using McpClient mcpClient = await McpClient.CreateAsync(new HttpClientTransport(new()
-{
-    Endpoint = new Uri("https://learn.microsoft.com/api/mcp"),
-    Name = "Microsoft Learn MCP",
-}));
-
-// Retrieve the list of tools available on the MCP server.
-IList<McpClientTool> mcpTools = await mcpClient.ListToolsAsync();
-Console.WriteLine($"MCP tools available: {string.Join(", ", mcpTools.Select(t => t.Name))}");
-
-List<AITool> agentTools = [.. mcpTools.Cast<AITool>()];
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
-
-AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
-    instructions: "You are a helpful assistant that can help with Microsoft documentation questions. Use the Microsoft Learn MCP tool to search for documentation.",
-    name: "DocsAgent",
-    tools: agentTools);
-
-Console.WriteLine($"Agent '{agent.Name}' created. Asking a question...\n");
-
-const string Prompt = "How does one create an Azure storage account using az cli?";
-Console.WriteLine($"User: {Prompt}\n");
-Console.WriteLine($"Agent: {await agent.RunAsync(Prompt)}");
@@ -1,29 +0,0 @@
-# Using MCP Client as Tools with the Responses API
-
-This sample shows how to use MCP (Model Context Protocol) client tools with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Connecting to an MCP server via HTTP client transport
- Retrieving MCP tools and passing them to a `ChatClientAgent`
- Using MCP tools for agent interactions without server-side agent creation
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
- Node.js installed (for npx/MCP server)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,21 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <None Update="assets\walkway.jpg">
-      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
-    </None>
-  </ItemGroup>
-
-</Project>
@@ -1,30 +0,0 @@
-# Using Images with the Responses API
-
-This sample demonstrates how to use image multi-modality with an agent.
-
-## What this sample demonstrates
-
- Loading images using `DataContent.LoadFromAsync`
- Sending images alongside text to the agent
- Streaming the agent's image analysis response
- No server-side agent creation or cleanup required
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and a vision-capable model deployment (e.g., `gpt-4o`)
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o"
-```
-
-## Run the sample
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step10_UsingImages
-```
@@ -1,15 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,30 +0,0 @@
-# Agent as a Function Tool with the Responses API
-
-This sample demonstrates how to use one agent as a function tool for another agent.
-
-## What this sample demonstrates
-
- Creating a specialized agent (weather) with function tools
- Exposing an agent as a function tool using `.AsAIFunction()`
- Composing agents where one agent delegates to another
- No server-side agent creation or cleanup required
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step11_AsFunctionTool
-```
@@ -1,31 +0,0 @@
-# Middleware with the Responses API
-
-This sample demonstrates multiple middleware layers working together: PII filtering, guardrails, function invocation logging, and human-in-the-loop approval.
-
-## What this sample demonstrates
-
- Agent-level run middleware (PII filtering, guardrail enforcement)
- Function-level middleware (logging, result overrides)
- Human-in-the-loop approval workflows for sensitive function calls
- Using `.AsBuilder().Use()` to compose middleware
- No server-side agent creation or cleanup required
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\Agent_Step12_Middleware
-```
@@ -1,153 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to use plugins with an AI agent. Plugin classes can
-// depend on other services that need to be injected. In this sample, the
-// AgentPlugin class uses the WeatherProvider and CurrentTimeProvider classes
-// to get weather and current time information. Both services are registered
-// in the service collection and injected into the plugin.
-// Plugin classes may have many methods, but only some are intended to be used
-// as AI functions. The AsAITools method of the plugin class shows how to specify
-// which methods should be exposed to the AI agent.
-
-using Azure.AI.Projects;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-using Microsoft.Extensions.DependencyInjection;
-using SampleApp;
-
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-const string AssistantInstructions = "You are a helpful assistant that helps people find information.";
-const string AssistantName = "PluginAssistant";
-
-// Create a service collection to hold the agent plugin and its dependencies.
-ServiceCollection services = new();
-services.AddSingleton<WeatherProvider>();
-services.AddSingleton<CurrentTimeProvider>();
-services.AddSingleton<AgentPlugin>(); // The plugin depends on WeatherProvider and CurrentTimeProvider registered above.
-
-IServiceProvider serviceProvider = services.BuildServiceProvider();
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
-
-// Create a ChatClientAgent with the options-based constructor to pass services.
-AIAgent agent = aiProjectClient.AsAIAgent(new ChatClientAgentOptions
-{
-    Name = AssistantName,
-    ChatOptions = new() { ModelId = deploymentName, Instructions = AssistantInstructions, Tools = serviceProvider.GetRequiredService<AgentPlugin>().AsAITools().ToList() }
-},
-    services: serviceProvider);
-
-// Invoke the agent and output the text result.
-AgentSession session = await agent.CreateSessionAsync();
-Console.WriteLine(await agent.RunAsync("Tell me current time and weather in Seattle.", session));
-
-namespace SampleApp
-{
-    /// <summary>
-    /// The agent plugin that provides weather and current time information.
-    /// </summary>
-    internal sealed class AgentPlugin
-    {
-        private readonly WeatherProvider _weatherProvider;
-
-        /// <summary>
-        /// Initializes a new instance of the <see cref="AgentPlugin"/> class.
-        /// </summary>
-        /// <param name="weatherProvider">The weather provider to get weather information.</param>
-        public AgentPlugin(WeatherProvider weatherProvider)
-        {
-            this._weatherProvider = weatherProvider;
-        }
-
-        /// <summary>
-        /// Gets the weather information for the specified location.
-        /// </summary>
-        /// <remarks>
-        /// This method demonstrates how to use the dependency that was injected into the plugin class.
-        /// </remarks>
-        /// <param name="location">The location to get the weather for.</param>
-        /// <returns>The weather information for the specified location.</returns>
-        public string GetWeather(string location)
-        {
-            return this._weatherProvider.GetWeather(location);
-        }
-
-        /// <summary>
-        /// Gets the current date and time for the specified location.
-        /// </summary>
-        /// <remarks>
-        /// This method demonstrates how to resolve a dependency using the service provider passed to the method.
-        /// </remarks>
-        /// <param name="sp">The service provider to resolve the <see cref="CurrentTimeProvider"/>.</param>
-        /// <param name="location">The location to get the current time for.</param>
-        /// <returns>The current date and time as a <see cref="DateTimeOffset"/>.</returns>
-        public DateTimeOffset GetCurrentTime(IServiceProvider sp, string location)
-        {
-            CurrentTimeProvider currentTimeProvider = sp.GetRequiredService<CurrentTimeProvider>();
-            return currentTimeProvider.GetCurrentTime(location);
-        }
-
-        /// <summary>
-        /// Returns the functions provided by this plugin.
-        /// </summary>
-        /// <remarks>
-        /// In real world scenarios, a class may have many methods and only a subset of them may be intended to be exposed as AI functions.
-        /// This method demonstrates how to explicitly specify which methods should be exposed to the AI agent.
-        /// </remarks>
-        /// <returns>The functions provided by this plugin.</returns>
-        public IEnumerable<AITool> AsAITools()
-        {
-            yield return AIFunctionFactory.Create(this.GetWeather);
-            yield return AIFunctionFactory.Create(this.GetCurrentTime);
-        }
-    }
-
-    internal sealed class WeatherProvider
-    {
-        private readonly string _weatherSummary = "cloudy with a high of 15°C";
-
-        /// <summary>
-        /// The weather provider that returns weather information.
-        /// </summary>
-        /// <summary>
-        /// Gets the weather information for the specified location.
-        /// </summary>
-        /// <remarks>
-        /// The weather information is hardcoded for demonstration purposes.
-        /// In a real application, this could call a weather API to get actual weather data.
-        /// </remarks>
-        /// <param name="location">The location to get the weather for.</param>
-        /// <returns>The weather information for the specified location.</returns>
-        public string GetWeather(string location)
-        {
-            return $"The weather in {location} is {this._weatherSummary}.";
-        }
-    }
-
-    internal sealed class CurrentTimeProvider
-    {
-        private readonly TimeProvider _timeProvider = TimeProvider.System;
-
-        /// <summary>
-        /// Provides the current date and time.
-        /// </summary>
-        /// <remarks>
-        /// This class returns the current date and time using the system's clock.
-        /// </remarks>
-        /// <summary>
-        /// Gets the current date and time.
-        /// </summary>
-        /// <param name="location">The location to get the current time for (not used in this implementation).</param>
-        /// <returns>The current date and time as a <see cref="DateTimeOffset"/>.</returns>
-        public DateTimeOffset GetCurrentTime(string location)
-        {
-            return this._timeProvider.GetLocalNow();
-        }
-    }
-}
@@ -1,29 +0,0 @@
-# Using Plugins with the Responses API
-
-This sample shows how to use plugins with a `ChatClientAgent` using the Responses API directly, with dependency injection for plugin services.
-
-## What this sample demonstrates
-
- Creating plugin classes with injected dependencies
- Registering services and building a service provider
- Passing `services` to the `ChatClientAgent` via the options-based constructor
- Using `AIFunctionFactory` to expose plugin methods as AI tools
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,28 +0,0 @@
-# Code Interpreter with the Responses API
-
-This sample shows how to use the Code Interpreter tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Using `HostedCodeInterpreterTool` with `ChatClientAgent`
- Extracting code input and output from agent responses
- Handling code interpreter annotations and file citations
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,29 +0,0 @@
-# Computer Use with the Responses API
-
-This sample shows how to use the Computer Use tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Using `FoundryAITool.CreateComputerTool()` with `ChatClientAgent`
- Processing computer call actions (click, type, key press)
- Managing the computer use interaction loop with screenshots
- Handling the Azure Agents API workaround for `previous_response_id` with `computer_call_output`
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="computer-use-preview"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,29 +0,0 @@
-# File Search with the Responses API
-
-This sample shows how to use the File Search tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Uploading files and creating vector stores via `AIProjectClient`
- Using `HostedFileSearchTool` with `ChatClientAgent`
- Handling file citation annotations in agent responses
- Cleaning up file resources after use
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,20 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,29 +0,0 @@
-# OpenAPI Tools with the Responses API
-
-This sample shows how to use OpenAPI tools with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Defining an OpenAPI specification inline
- Creating an `OpenAPIFunctionDefinition` for the REST Countries API
- Using `FoundryAITool.CreateOpenApiTool()` with `ChatClientAgent`
- Server-side execution of OpenAPI tool calls
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,36 +0,0 @@
-# Bing Custom Search with the Responses API
-
-This sample shows how to use the Bing Custom Search tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Configuring `BingCustomSearchToolParameters` with connection ID and instance name
- Using `FoundryAITool.CreateBingCustomSearchTool()` with `ChatClientAgent`
- Processing search results from agent responses
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
- Bing Custom Search resource configured with a connection ID
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-$env:AZURE_AI_CUSTOM_SEARCH_CONNECTION_ID="your-connection-id"  # The full ARM resource URI, e.g., "/subscriptions/.../connections/your-bing-connection"
-$env:AZURE_AI_CUSTOM_SEARCH_INSTANCE_NAME="your-instance-name"  # The Bing Custom Search configuration name (from Azure portal)
-```
-
-### Finding the connection ID and instance name
-
- **Connection ID** (`AZURE_AI_CUSTOM_SEARCH_CONNECTION_ID`): The full ARM resource URI including the `/projects/<name>/connections/<connection-name>` segment. Find the connection name in your Foundry project under **Management center** → **Connected resources**.
- **Instance Name** (`AZURE_AI_CUSTOM_SEARCH_INSTANCE_NAME`): The **configuration name** from your Bing Custom Search resource (Azure portal → your Bing Custom Search resource → **Configurations**). This is _not_ the Azure resource name or the connection name — it's the name of the specific search configuration that defines which domains/sites to search against.
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,19 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,30 +0,0 @@
-# SharePoint Grounding with the Responses API
-
-This sample shows how to use the SharePoint Grounding tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Configuring `SharePointGroundingToolOptions` with project connections
- Using `FoundryAITool.CreateSharepointTool()` with `ChatClientAgent`
- Displaying grounding annotations from agent responses
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
- SharePoint connection configured in your Microsoft Foundry project
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-$env:SHAREPOINT_PROJECT_CONNECTION_ID="your-sharepoint-connection-id"  # The full ARM resource URI, e.g., "/subscriptions/.../connections/SharepointTestTool"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,19 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,42 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to use Microsoft Fabric Tool with a ChatClientAgent.
-
-using Azure.AI.Projects;
-using Azure.AI.Projects.Agents;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Agents.AI.AzureAI;
-
-string fabricConnectionId = Environment.GetEnvironmentVariable("FABRIC_PROJECT_CONNECTION_ID") ?? throw new InvalidOperationException("FABRIC_PROJECT_CONNECTION_ID is not set.");
-
-const string AgentInstructions = "You are a helpful assistant with access to Microsoft Fabric data. Answer questions based on data available through your Fabric connection.";
-
-// Configure Microsoft Fabric tool options with project connection
-var fabricToolOptions = new FabricDataAgentToolOptions();
-fabricToolOptions.ProjectConnections.Add(new ToolProjectConnection(fabricConnectionId));
-
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
-
-// Create a AIAgent with Microsoft Fabric tool.
-AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
-    instructions: AgentInstructions,
-    name: "FabricAgent-RAPI",
-    tools: [FoundryAITool.CreateMicrosoftFabricTool(fabricToolOptions)]);
-
-Console.WriteLine($"Created agent: {agent.Name}");
-
-// Run the agent with a sample query
-AgentResponse response = await agent.RunAsync("What data is available in the connected Fabric workspace?");
-
-Console.WriteLine("\n=== Agent Response ===");
-foreach (var message in response.Messages)
-{
-    Console.WriteLine(message.Text);
-}
@@ -1,30 +0,0 @@
-# Microsoft Fabric with the Responses API
-
-This sample shows how to use the Microsoft Fabric tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Configuring `FabricDataAgentToolOptions` with project connections
- Using `FoundryAITool.CreateMicrosoftFabricTool()` with `ChatClientAgent`
- Querying data available through a Fabric connection
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
- Microsoft Fabric connection configured in your Microsoft Foundry project
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-$env:FABRIC_PROJECT_CONNECTION_ID="your-fabric-connection-id"  # The full ARM resource URI, e.g., "/subscriptions/.../connections/FabricTestTool"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,19 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,44 +0,0 @@
-// Copyright (c) Microsoft. All rights reserved.
-
-// This sample shows how to use the Web Search Tool with a ChatClientAgent.
-
-using Azure.AI.Projects;
-using Azure.Identity;
-using Microsoft.Agents.AI;
-using Microsoft.Extensions.AI;
-using OpenAI.Responses;
-
-const string AgentInstructions = "You are a helpful assistant that can search the web to find current information and answer questions accurately.";
-const string AgentName = "WebSearchAgent-RAPI";
-
-string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
-string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
-
-// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
-// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
-// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
-
-// Create a AIAgent with HostedWebSearchTool.
-AIAgent agent = aiProjectClient.AsAIAgent(deploymentName,
-    instructions: AgentInstructions,
-    name: AgentName,
-    tools: [new HostedWebSearchTool()]);
-
-AgentResponse response = await agent.RunAsync("What's the weather today in Seattle?");
-
-// Get the text response
-Console.WriteLine($"Response: {response.Text}");
-
-// Getting any annotations/citations generated by the web search tool
-foreach (AIAnnotation annotation in response.Messages.SelectMany(m => m.Contents).SelectMany(c => c.Annotations ?? []))
-{
-    Console.WriteLine($"Annotation: {annotation}");
-    if (annotation.RawRepresentation is UriCitationMessageAnnotation urlCitation)
-    {
-        Console.WriteLine($$"""
-            Title: {{urlCitation.Title}}
-            URL: {{urlCitation.Uri}}
-            """);
-    }
-}
@@ -1,28 +0,0 @@
-# Web Search with the Responses API
-
-This sample shows how to use the Web Search tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Using `HostedWebSearchTool` with `ChatClientAgent`
- Processing web search citations and annotations
- Extracting URL citation details (title, URL) from responses
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,20 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFrameworks>net10.0</TargetFrameworks>
-
-    <Nullable>enable</Nullable>
-    <ImplicitUsings>enable</ImplicitUsings>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Azure.Identity" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
-    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
-  </ItemGroup>
-
-</Project>
@@ -1,31 +0,0 @@
-# Memory Search with the Responses API
-
-This sample demonstrates how to use the Memory Search tool with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Configuring `MemorySearchPreviewTool` with a memory store and user scope
- Using memory search for cross-conversation recall
- Inspecting `MemorySearchToolCallResponseItem` results
- User profile persistence across conversations
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
- A memory store created beforehand via Azure Portal or Python SDK
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-$env:AZURE_AI_MEMORY_STORE_ID="your-memory-store-name"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,29 +0,0 @@
-# Local MCP with the Responses API
-
-This sample demonstrates how to use a local MCP (Model Context Protocol) client with a `ChatClientAgent` using the Responses API directly.
-
-## What this sample demonstrates
-
- Connecting to an MCP server via HTTP (Streamable HTTP transport)
- Resolving MCP tools locally and wrapping them with logging
- Using `DelegatingAIFunction` to add custom behavior to MCP tools
- Passing locally-resolved MCP tools to `ChatClientAgent`
-
-## Prerequisites
-
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- Azure CLI installed and authenticated (`az login`)
-
-Set the following environment variables:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-## Run the sample
-
-```powershell
-dotnet run
-```
@@ -1,81 +0,0 @@
-# Getting started with Foundry Agents
-
-These samples demonstrate how to use Azure AI Foundry with Agent Framework.
-
-## Quick start
-
-The simplest way to create a Foundry agent is using the `FoundryAgent` type directly:
-
-```csharp
-FoundryAgent agent = new(
-    new Uri(endpoint),
-    new AzureCliCredential(),
-    model: "gpt-4o-mini",
-    instructions: "You are good at telling jokes.",
-    name: "JokerAgent");
-
-Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
-```
-
-Or using the `AIProjectClient.AsAIAgent(...)` extensions:
-
-```csharp
-AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
-
-FoundryAgent agent = aiProjectClient.AsAIAgent(
-    model: deploymentName,
-    instructions: "You are good at telling jokes.",
-    name: "JokerAgent");
-```
-
-## Prerequisites
-
- .NET 10 SDK or later
- Foundry project endpoint
- Azure CLI installed and authenticated
-
-Set:
-
-```powershell
-$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
-$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
-```
-
-Some samples require extra tool-specific environment variables. See each sample for details.
-
-## Samples
-
-| Sample | Description |
-| --- | --- |
-| [FoundryAgent lifecycle](./Agent_Step00_FoundryAgentLifecycle/) | Create a FoundryAgent directly with endpoint and credentials |
-| [Basics (Responses API)](./Agent_Step01_Basics/) | Create and run an agent using AsAIAgent extensions |
-| [Multi-turn conversation](./Agent_Step02.1_MultiturnConversation/) | Multi-turn using sessions and response ID chaining |
-| [Multi-turn with server conversations](./Agent_Step02.2_MultiturnWithServerConversations/) | Server-side conversations visible in Foundry UI |
-| [Using function tools](./Agent_Step03_UsingFunctionTools/) | Function tools |
-| [Function tools with approvals](./Agent_Step04_UsingFunctionToolsWithApprovals/) | Human-in-the-loop approval |
-| [Structured output](./Agent_Step05_StructuredOutput/) | Structured output with JSON schema |
-| [Persisted conversations](./Agent_Step06_PersistedConversations/) | Persisting and resuming conversations |
-| [Observability](./Agent_Step07_Observability/) | OpenTelemetry observability |
-| [Dependency injection](./Agent_Step08_DependencyInjection/) | DI with a hosted service |
-| [Using MCP client as tools](./Agent_Step09_UsingMcpClientAsTools/) | MCP client tools |
-| [Using images](./Agent_Step10_UsingImages/) | Image multi-modality |
-| [Agent as function tool](./Agent_Step11_AsFunctionTool/) | Agent as a function tool for another |
-| [Middleware](./Agent_Step12_Middleware/) | Multiple middleware layers |
-| [Plugins](./Agent_Step13_Plugins/) | Plugins with dependency injection |
-| [Code interpreter](./Agent_Step14_CodeInterpreter/) | Code interpreter tool |
-| [Computer use](./Agent_Step15_ComputerUse/) | Computer use tool |
-| [File search](./Agent_Step16_FileSearch/) | File search tool |
-| [OpenAPI tools](./Agent_Step17_OpenAPITools/) | OpenAPI tools |
-| [Bing custom search](./Agent_Step18_BingCustomSearch/) | Bing Custom Search tool |
-| [SharePoint](./Agent_Step19_SharePoint/) | SharePoint grounding tool |
-| [Microsoft Fabric](./Agent_Step20_MicrosoftFabric/) | Microsoft Fabric tool |
-| [Web search](./Agent_Step21_WebSearch/) | Web search tool |
-| [Memory search](./Agent_Step22_MemorySearch/) | Memory search tool |
-| [Local MCP](./Agent_Step23_LocalMCP/) | Local MCP client with HTTP transport |
-
-## Running the samples
-
-```powershell
-cd dotnet/samples/02-agents/AgentsWithFoundry
-dotnet run --project .\FoundryAgent_Step01
-```
@@ -9,7 +9,8 @@
  </PropertyGroup>

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

 </Project>
@@ -0,0 +1,100 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use Azure AI Foundry's Red Teaming service to assess
+// the safety and resilience of an AI model against adversarial attacks.
+//
+// It uses the RedTeam API from Azure.AI.Projects to run automated attack simulations
+// with various attack strategies (encoding, obfuscation, jailbreaks) across multiple
+// risk categories (Violence, HateUnfairness, Sexual, SelfHarm).
+//
+// For more details, see:
+// https://learn.microsoft.com/azure/ai-foundry/concepts/ai-red-teaming-agent
+
+using Azure.AI.Projects;
+using Azure.Identity;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+Console.WriteLine("=" + new string('=', 79));
+Console.WriteLine("RED TEAMING EVALUATION SAMPLE");
+Console.WriteLine("=" + new string('=', 79));
+Console.WriteLine();
+
+// Initialize Azure credentials and clients
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+DefaultAzureCredential credential = new();
+AIProjectClient aiProjectClient = new(new Uri(endpoint), credential);
+
+// Configure the target model for red teaming
+AzureOpenAIModelConfiguration targetConfig = new(deploymentName);
+
+// Create the red team run configuration
+RedTeam redTeamConfig = new(targetConfig)
+{
+    DisplayName = "FinancialAdvisor-RedTeam",
+    ApplicationScenario = "A financial advisor assistant that provides general financial advice and information.",
+    NumTurns = 3,
+    RiskCategories =
+    {
+        RiskCategory.Violence,
+        RiskCategory.HateUnfairness,
+        RiskCategory.Sexual,
+        RiskCategory.SelfHarm,
+    },
+    AttackStrategies =
+    {
+        AttackStrategy.Easy,
+        AttackStrategy.Moderate,
+        AttackStrategy.Jailbreak,
+    },
+};
+
+Console.WriteLine($"Target model: {deploymentName}");
+Console.WriteLine("Risk categories: Violence, HateUnfairness, Sexual, SelfHarm");
+Console.WriteLine("Attack strategies: Easy, Moderate, Jailbreak");
+Console.WriteLine($"Simulation turns: {redTeamConfig.NumTurns}");
+Console.WriteLine();
+
+// Submit the red team run to the service
+Console.WriteLine("Submitting red team run...");
+RedTeam redTeamRun = await aiProjectClient.RedTeams.CreateAsync(redTeamConfig, options: null);
+
+Console.WriteLine($"Red team run created: {redTeamRun.Name}");
+Console.WriteLine($"Status: {redTeamRun.Status}");
+Console.WriteLine();
+
+// Poll for completion
+Console.WriteLine("Waiting for red team run to complete (this may take several minutes)...");
+while (redTeamRun.Status != "Completed" && redTeamRun.Status != "Failed" && redTeamRun.Status != "Canceled")
+{
+    await Task.Delay(TimeSpan.FromSeconds(15));
+    redTeamRun = await aiProjectClient.RedTeams.GetAsync(redTeamRun.Name);
+    Console.WriteLine($"  Status: {redTeamRun.Status}");
+}
+
+Console.WriteLine();
+
+if (redTeamRun.Status == "Completed")
+{
+    Console.WriteLine("Red team run completed successfully!");
+    Console.WriteLine();
+    Console.WriteLine("Results:");
+    Console.WriteLine(new string('-', 80));
+    Console.WriteLine($"  Run name:    {redTeamRun.Name}");
+    Console.WriteLine($"  Display name: {redTeamRun.DisplayName}");
+    Console.WriteLine($"  Status:      {redTeamRun.Status}");
+
+    Console.WriteLine();
+    Console.WriteLine("Review the detailed results in the Azure AI Foundry portal:");
+    Console.WriteLine($"  {endpoint}");
+}
+else
+{
+    Console.WriteLine($"Red team run ended with status: {redTeamRun.Status}");
+}
+
+Console.WriteLine();
+Console.WriteLine(new string('=', 80));
@@ -0,0 +1,101 @@
+# Red Teaming with Azure AI Foundry (Classic)
+
+> [!IMPORTANT]
+> This sample uses the **classic Azure AI Foundry** red teaming API (`/redTeams/runs`) via `Azure.AI.Projects`. Results are viewable in the classic Foundry portal experience. The **new Foundry** portal's red teaming feature uses a different evaluation-based API that is not yet available in the .NET SDK.
+
+This sample demonstrates how to use Azure AI Foundry's Red Teaming service to assess the safety and resilience of an AI model against adversarial attacks.
+
+## What this sample demonstrates
+
+- Configuring a red team run targeting an Azure OpenAI model deployment
+- Using multiple `AttackStrategy` options (Easy, Moderate, Jailbreak)
+- Evaluating across `RiskCategory` categories (Violence, HateUnfairness, Sexual, SelfHarm)
+- Submitting a red team scan and polling for completion
+- Reviewing results in the Azure AI Foundry portal
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure AI Foundry project (hub and project created)
+- Azure OpenAI deployment (e.g., gpt-4o or gpt-4o-mini)
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+### Regional Requirements
+
+Red teaming is only available in regions that support risk and safety evaluators:
+- **East US 2**, **Sweden Central**, **US North Central**, **France Central**, **Switzerland West**
+
+### Environment Variables
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/api/projects/your-project" # Replace with your Azure Foundry project endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+```
+
+## Run the sample
+
+Navigate to the sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step01_RedTeaming
+dotnet run
+```
+
+## Expected behavior
+
+The sample will:
+
+1. Configure a `RedTeam` run targeting the specified model deployment
+2. Define risk categories and attack strategies
+3. Submit the scan to Azure AI Foundry's Red Teaming service
+4. Poll for completion (this may take several minutes)
+5. Display the run status and direct you to the Azure AI Foundry portal for detailed results
+
+## Understanding Red Teaming
+
+### Attack Strategies
+
+| Strategy | Description |
+|----------|-------------|
+| Easy | Simple encoding/obfuscation attacks (ROT13, Leetspeak, etc.) |
+| Moderate | Moderate complexity attacks requiring an LLM for orchestration |
+| Jailbreak | Crafted prompts designed to bypass AI safeguards (UPIA) |
+
+### Risk Categories
+
+| Category | Description |
+|----------|-------------|
+| Violence | Content related to violence |
+| HateUnfairness | Hate speech or unfair content |
+| Sexual | Sexual content |
+| SelfHarm | Self-harm related content |
+
+### Interpreting Results
+
+- Results are available in the Azure AI Foundry portal (**classic view** — toggle at top-right) under the red teaming section
+- Lower Attack Success Rate (ASR) is better — target ASR < 5% for production
+- Review individual attack conversations to understand vulnerabilities
+
+### Current Limitations
+
+> [!NOTE]
+> - The .NET Red Teaming API (`Azure.AI.Projects`) currently supports targeting **model deployments only** via `AzureOpenAIModelConfiguration`. The `AzureAIAgentTarget` type exists in the SDK but is consumed by the **Evaluation Taxonomy** API (`/evaluationtaxonomies`), not by the Red Teaming API (`/redTeams/runs`).
+> - Agent-targeted red teaming with agent-specific risk categories (Prohibited actions, Sensitive data leakage, Task adherence) is documented in the [concept docs](https://learn.microsoft.com/azure/ai-foundry/concepts/ai-red-teaming-agent) but is not yet available via the public REST API or .NET SDK.
+> - Results from this API appear in the **classic** Azure AI Foundry portal view. The new Foundry portal uses a separate evaluation-based system with `eval_*` identifiers.
+
+## Related Resources
+
+- [Azure AI Red Teaming Agent](https://learn.microsoft.com/azure/ai-foundry/concepts/ai-red-teaming-agent)
+- [RedTeam .NET API Reference](https://learn.microsoft.com/dotnet/api/azure.ai.projects.redteam?view=azure-dotnet-preview)
+- [Risk and Safety Evaluations](https://learn.microsoft.com/azure/ai-foundry/concepts/evaluation-metrics-built-in#risk-and-safety-evaluators)
+
+## Next Steps
+
+After running red teaming:
+1. Review attack results and strengthen agent guardrails
+2. Explore the Self-Reflection sample (FoundryAgents_Evaluations_Step02_SelfReflection) for quality assessment
+3. Set up continuous red teaming in your CI/CD pipeline
@@ -0,0 +1,25 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.AI.Projects" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.Evaluation" />
+    <PackageReference Include="Microsoft.Extensions.AI.Evaluation.Quality" />
+    <PackageReference Include="Microsoft.Extensions.AI.Evaluation.Safety" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,292 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use Microsoft.Extensions.AI.Evaluation.Quality to evaluate
+// an Agent Framework agent's response quality with a self-reflection loop.
+//
+// It uses GroundednessEvaluator, RelevanceEvaluator, and CoherenceEvaluator to score responses,
+// then iteratively asks the agent to improve based on evaluation feedback.
+//
+// Based on: Reflexion: Language Agents with Verbal Reinforcement Learning (NeurIPS 2023)
+// Reference: https://arxiv.org/abs/2303.11366
+//
+// For more details, see:
+// https://learn.microsoft.com/dotnet/ai/evaluation/libraries
+
+using Azure.AI.OpenAI;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.AI.Evaluation;
+using Microsoft.Extensions.AI.Evaluation.Quality;
+using Microsoft.Extensions.AI.Evaluation.Safety;
+
+using ChatMessage = Microsoft.Extensions.AI.ChatMessage;
+using ChatRole = Microsoft.Extensions.AI.ChatRole;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+string openAiEndpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string evaluatorDeploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? deploymentName;
+
+Console.WriteLine("=" + new string('=', 79));
+Console.WriteLine("SELF-REFLECTION EVALUATION SAMPLE");
+Console.WriteLine("=" + new string('=', 79));
+Console.WriteLine();
+
+// Initialize Azure credentials and client
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+DefaultAzureCredential credential = new();
+AIProjectClient aiProjectClient = new(new Uri(endpoint), credential);
+
+// Set up the LLM-based chat client for quality evaluators
+IChatClient chatClient = new AzureOpenAIClient(new Uri(openAiEndpoint), credential)
+    .GetChatClient(evaluatorDeploymentName)
+    .AsIChatClient();
+
+// Configure evaluation: quality evaluators use the LLM, safety evaluators use Azure AI Foundry
+ContentSafetyServiceConfiguration safetyConfig = new(
+    credential: credential,
+    endpoint: new Uri(endpoint));
+
+ChatConfiguration chatConfiguration = safetyConfig.ToChatConfiguration(
+    originalChatConfiguration: new ChatConfiguration(chatClient));
+
+// Create a test agent
+AIAgent agent = await aiProjectClient.CreateAIAgentAsync(
+    name: "KnowledgeAgent",
+    model: deploymentName,
+    instructions: "You are a helpful assistant. Answer questions accurately based on the provided context.");
+Console.WriteLine($"Created agent: {agent.Name}");
+Console.WriteLine();
+
+// Example question and grounding context
+const string Question = """
+    What are the main benefits of using Azure AI Foundry for building AI applications?
+    """;
+
+const string Context = """
+    Azure AI Foundry is a comprehensive platform for building, deploying, and managing AI applications.
+    Key benefits include:
+    1. Unified development environment with support for multiple AI frameworks and models
+    2. Built-in safety and security features including content filtering and red teaming tools
+    3. Scalable infrastructure that handles deployment and monitoring automatically
+    4. Integration with Azure services like Azure OpenAI, Cognitive Services, and Machine Learning
+    5. Evaluation tools for assessing model quality, safety, and performance
+    6. Support for RAG (Retrieval-Augmented Generation) patterns with vector search
+    7. Enterprise-grade compliance and governance features
+    """;
+
+Console.WriteLine("Question:");
+Console.WriteLine(Question);
+Console.WriteLine();
+
+// Run evaluations
+try
+{
+    await RunSelfReflectionWithGroundedness(agent, Question, Context, chatConfiguration);
+    await RunQualityEvaluation(agent, Question, Context, chatConfiguration);
+    await RunCombinedQualityAndSafetyEvaluation(agent, Question, chatConfiguration);
+}
+finally
+{
+    // Cleanup
+    await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
+    Console.WriteLine();
+    Console.WriteLine("Cleanup: Agent deleted.");
+}
+
+// ============================================================================
+// Implementation Functions
+// ============================================================================
+
+static async Task RunSelfReflectionWithGroundedness(
+    AIAgent agent, string question, string context, ChatConfiguration chatConfiguration)
+{
+    Console.WriteLine("Running Self-Reflection with Groundedness Evaluation...");
+    Console.WriteLine();
+
+    GroundednessEvaluator groundednessEvaluator = new();
+    GroundednessEvaluatorContext groundingContext = new(context);
+
+    const int MaxReflections = 3;
+    double bestScore = 0;
+
+    string currentPrompt = $"Context: {context}\n\nQuestion: {question}";
+
+    for (int i = 0; i < MaxReflections; i++)
+    {
+        Console.WriteLine($"Iteration {i + 1}/{MaxReflections}:");
+        Console.WriteLine(new string('-', 40));
+
+        // Create a new session for each reflection iteration so that
+        // conversation context does not carry over between runs. This keeps
+        // each evaluation independent and avoids biasing groundedness scores.
+        AgentSession session = await agent.CreateSessionAsync();
+        AgentResponse agentResponse = await agent.RunAsync(currentPrompt, session);
+        string responseText = agentResponse.Text;
+
+        Console.WriteLine($"Response: {responseText[..Math.Min(150, responseText.Length)]}...");
+
+        List<ChatMessage> messages =
+        [
+            new(ChatRole.User, currentPrompt),
+        ];
+        ChatResponse chatResponse = new(new ChatMessage(ChatRole.Assistant, responseText));
+
+        EvaluationResult result = await groundednessEvaluator.EvaluateAsync(
+            messages,
+            chatResponse,
+            chatConfiguration,
+            additionalContext: [groundingContext]);
+
+        NumericMetric groundedness = result.Get<NumericMetric>(GroundednessEvaluator.GroundednessMetricName);
+        double score = groundedness.Value ?? 0;
+        string rating = groundedness.Interpretation?.Rating.ToString() ?? "N/A";
+
+        Console.WriteLine($"Groundedness score: {score:F1}/5 (Rating: {rating})");
+        Console.WriteLine();
+
+        if (score > bestScore)
+        {
+            bestScore = score;
+        }
+
+        if (score >= 4.0 || i == MaxReflections - 1)
+        {
+            if (score >= 4.0)
+            {
+                Console.WriteLine("Good groundedness achieved!");
+            }
+
+            break;
+        }
+
+        // Ask for improvement in the next iteration, including the previous response
+        // so the LLM knows what to improve on (each iteration uses a new session).
+        currentPrompt = $"""
+            Context: {context}
+
+            Your previous answer scored {score}/5 on groundedness.
+            Your previous answer was:
+            {responseText}
+
+            Please improve your answer to be more grounded in the provided context.
+            Only include information that is directly supported by the context.
+
+            Question: {question}
+            """;
+        Console.WriteLine("Requesting improvement...");
+        Console.WriteLine();
+    }
+
+    Console.WriteLine($"Best groundedness score: {bestScore:F1}/5");
+    Console.WriteLine(new string('=', 80));
+    Console.WriteLine();
+}
+
+static async Task RunQualityEvaluation(
+    AIAgent agent, string question, string context, ChatConfiguration chatConfiguration)
+{
+    Console.WriteLine("Running Quality Evaluation (Relevance, Coherence, Groundedness)...");
+    Console.WriteLine();
+
+    IEvaluator[] evaluators =
+    [
+        new RelevanceEvaluator(),
+        new CoherenceEvaluator(),
+        new GroundednessEvaluator(),
+    ];
+
+    CompositeEvaluator compositeEvaluator = new(evaluators);
+    GroundednessEvaluatorContext groundingContext = new(context);
+
+    string prompt = $"Context: {context}\n\nQuestion: {question}";
+
+    AgentSession session = await agent.CreateSessionAsync();
+    AgentResponse agentResponse = await agent.RunAsync(prompt, session);
+    string responseText = agentResponse.Text;
+
+    Console.WriteLine($"Response: {responseText[..Math.Min(150, responseText.Length)]}...");
+    Console.WriteLine();
+
+    List<ChatMessage> messages =
+    [
+        new(ChatRole.User, prompt),
+    ];
+    ChatResponse chatResponse = new(new ChatMessage(ChatRole.Assistant, responseText));
+
+    EvaluationResult result = await compositeEvaluator.EvaluateAsync(
+        messages,
+        chatResponse,
+        chatConfiguration,
+        additionalContext: [groundingContext]);
+
+    foreach (EvaluationMetric metric in result.Metrics.Values)
+    {
+        if (metric is NumericMetric n)
+        {
+            string rating = n.Interpretation?.Rating.ToString() ?? "N/A";
+            Console.WriteLine($"  {n.Name,-20} Score: {n.Value:F1}/5  Rating: {rating}");
+        }
+    }
+
+    Console.WriteLine(new string('=', 80));
+    Console.WriteLine();
+}
+
+static async Task RunCombinedQualityAndSafetyEvaluation(
+    AIAgent agent, string question, ChatConfiguration chatConfiguration)
+{
+    Console.WriteLine("Running Combined Quality + Safety Evaluation...");
+    Console.WriteLine();
+
+    IEvaluator[] evaluators =
+    [
+        new RelevanceEvaluator(),
+        new CoherenceEvaluator(),
+        new ContentHarmEvaluator(),
+        new ProtectedMaterialEvaluator(),
+    ];
+
+    CompositeEvaluator compositeEvaluator = new(evaluators);
+
+    AgentSession session = await agent.CreateSessionAsync();
+    AgentResponse agentResponse = await agent.RunAsync(question, session);
+    string responseText = agentResponse.Text;
+
+    Console.WriteLine($"Response: {responseText[..Math.Min(150, responseText.Length)]}...");
+    Console.WriteLine();
+
+    List<ChatMessage> messages =
+    [
+        new(ChatRole.User, question), // No context in this evaluation — testing quality and safety on raw question
+    ];
+    ChatResponse chatResponse = new(new ChatMessage(ChatRole.Assistant, responseText));
+
+    EvaluationResult result = await compositeEvaluator.EvaluateAsync(
+        messages,
+        chatResponse,
+        chatConfiguration);
+
+    Console.WriteLine("Quality Metrics:");
+    foreach (EvaluationMetric metric in result.Metrics.Values)
+    {
+        if (metric is NumericMetric n)
+        {
+            string rating = n.Interpretation?.Rating.ToString() ?? "N/A";
+            bool failed = n.Interpretation?.Failed ?? false;
+            Console.WriteLine($"  {n.Name,-25} Score: {n.Value:F1,-6} Rating: {rating,-15} Failed: {failed}");
+        }
+        else if (metric is BooleanMetric b)
+        {
+            string rating = b.Interpretation?.Rating.ToString() ?? "N/A";
+            bool failed = b.Interpretation?.Failed ?? false;
+            Console.WriteLine($"  {b.Name,-25} Value: {b.Value,-6} Rating: {rating,-15} Failed: {failed}");
+        }
+    }
+
+    Console.WriteLine(new string('=', 80));
+}
@@ -0,0 +1,118 @@
+# Self-Reflection Evaluation with Groundedness Assessment
+
+This sample demonstrates the self-reflection pattern using Agent Framework with `Microsoft.Extensions.AI.Evaluation.Quality` evaluators. The agent iteratively improves its responses based on real groundedness evaluation scores.
+
+For details on the self-reflection approach, see [Reflexion: Language Agents with Verbal Reinforcement Learning](https://arxiv.org/abs/2303.11366) (NeurIPS 2023).
+
+## What this sample demonstrates
+
+- Self-reflection loop that improves responses using real `GroundednessEvaluator` scores
+- Using `RelevanceEvaluator` and `CoherenceEvaluator` for multi-metric quality assessment
+- Combining quality and safety evaluators with `CompositeEvaluator`
+- Configuring `ContentSafetyServiceConfiguration` for safety evaluators alongside LLM-based quality evaluators
+- Tracking improvement across iterations
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure AI Foundry project (hub and project created)
+- Azure OpenAI deployment (e.g., gpt-4o or gpt-4o-mini)
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+### Azure Resources Required
+
+1. **Azure AI Hub and Project**: Create these in the Azure Portal
+   - Follow: https://learn.microsoft.com/azure/ai-foundry/how-to/create-projects
+2. **Azure OpenAI Deployment**: Deploy a model (e.g., gpt-4o or gpt-4o-mini)
+   - Agent model: Used to generate responses
+   - Evaluator model: Quality evaluators use an LLM; best results with GPT-4o
+3. **Azure CLI**: Install and authenticate with `az login`
+
+### Environment Variables
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-project.api.azureml.ms"  # Azure Foundry project endpoint
+$env:AZURE_OPENAI_ENDPOINT="https://your-openai.openai.azure.com/"         # Azure OpenAI endpoint (for quality evaluators)
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"                   # Model deployment name
+```
+
+**Note**: For best evaluation results, use GPT-4o or GPT-4o-mini as the evaluator model. The groundedness evaluator has been tested and tuned for these models.
+
+## Run the sample
+
+Navigate to the sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents/FoundryAgents_Evaluations_Step02_SelfReflection
+dotnet run
+```
+
+## Expected behavior
+
+The sample runs three evaluation scenarios:
+
+### 1. Self-Reflection with Groundedness
+- Asks a question with grounding context
+- Evaluates response groundedness using `GroundednessEvaluator`
+- If score is below 4/5, asks the agent to improve with feedback
+- Repeats up to 3 iterations
+- Tracks and reports the best score achieved
+
+### 2. Quality Evaluation
+- Evaluates a single response with multiple quality evaluators:
+  - `RelevanceEvaluator` — is the response relevant to the question?
+  - `CoherenceEvaluator` — is the response logically coherent?
+  - `GroundednessEvaluator` — is the response grounded in the provided context?
+
+### 3. Combined Quality + Safety Evaluation
+- Runs both quality and safety evaluators together:
+  - `RelevanceEvaluator`, `CoherenceEvaluator` (quality)
+  - `ContentHarmEvaluator` (safety — violence, hate, sexual, self-harm)
+  - `ProtectedMaterialEvaluator` (safety — copyrighted content detection)
+
+## Understanding the Evaluation
+
+### Groundedness Score (1-5 scale)
+
+The `GroundednessEvaluator` measures how well the agent's response is grounded in the provided context:
+
+- **5** = Excellent - Response is fully grounded in context
+- **4** = Good - Mostly grounded with minor deviations
+- **3** = Fair - Partially grounded but includes unsupported claims
+- **2** = Poor - Significant amount of ungrounded content
+- **1** = Very Poor - Response is largely unsupported by context
+
+### Self-Reflection Process
+
+1. **Initial Response**: Agent generates answer based on question + context
+2. **Evaluation**: `GroundednessEvaluator` scores the response (1-5)
+3. **Feedback**: If score < 4, agent receives the score and is asked to improve
+4. **Iteration**: Process repeats until good score or max iterations
+
+## Best Practices
+
+1. **Provide Complete Context**: Ensure grounding context contains all information needed to answer the question
+2. **Clear Instructions**: Give the agent clear instructions about staying grounded in context
+3. **Use Quality Models**: GPT-4o recommended for evaluation tasks
+4. **Multiple Evaluators**: Use combination of evaluators (groundedness + relevance + coherence)
+5. **Batch Processing**: For production, process multiple questions in batch
+
+## Related Resources
+
+- [Reflexion Paper (NeurIPS 2023)](https://arxiv.org/abs/2303.11366)
+- [Microsoft.Extensions.AI.Evaluation Libraries](https://learn.microsoft.com/dotnet/ai/evaluation/libraries)
+- [GroundednessEvaluator API Reference](https://learn.microsoft.com/dotnet/api/microsoft.extensions.ai.evaluation.quality.groundednessevaluator)
+- [Azure AI Foundry Evaluation Service](https://learn.microsoft.com/azure/ai-foundry/how-to/develop/evaluate-sdk)
+
+## Next Steps
+
+After running self-reflection evaluation:
+1. Implement similar patterns for other quality metrics (relevance, coherence, fluency)
+2. Integrate into CI/CD pipeline for continuous quality assurance
+3. Explore the Safety Evaluation sample (FoundryAgents_Evaluations_Step01_RedTeaming) for content safety assessment
@@ -0,0 +1,21 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <NoWarn>$(NoWarn);IDE0059</NoWarn>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.Projects" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,50 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create and use AI agents with Azure Foundry Agents as the backend.
+
+using Azure.AI.Projects;
+using Azure.AI.Projects.Agents;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+const string JokerName = "JokerAgent";
+
+// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Define the agent you want to create. (Prompt Agent in this case)
+AgentVersionCreationOptions options = new(new PromptAgentDefinition(model: deploymentName) { Instructions = "You are good at telling jokes." });
+
+// Azure.AI.Agents SDK creates and manages agent by name and versions.
+// You can create a server side agent version with the Azure.AI.Agents SDK client below.
+AgentVersion createdAgentVersion = aiProjectClient.Agents.CreateAgentVersion(agentName: JokerName, options);
+
+// Note:
+//      agentVersion.Id = "<agentName>:<versionNumber>",
+//      agentVersion.Version = <versionNumber>,
+//      agentVersion.Name = <agentName>
+
+// You can use an AIAgent with an already created server side agent version.
+AIAgent existingJokerAgent = aiProjectClient.AsAIAgent(createdAgentVersion);
+
+// You can also create another AIAgent version by providing the same name with a different definition/instruction.
+AIAgent newJokerAgent = await aiProjectClient.CreateAIAgentAsync(name: JokerName, model: deploymentName, instructions: "You are extremely hilarious at telling jokes.");
+
+// You can also get the AIAgent latest version by just providing its name.
+AIAgent jokerAgentLatest = await aiProjectClient.GetAIAgentAsync(name: JokerName);
+AgentVersion latestAgentVersion = jokerAgentLatest.GetService<AgentVersion>()!;
+
+// The AIAgent version can be accessed via the GetService method.
+Console.WriteLine($"Latest agent version id: {latestAgentVersion.Id}");
+
+// Once you have the AIAgent, you can invoke it like any other AIAgent.
+Console.WriteLine(await jokerAgentLatest.RunAsync("Tell me a joke about a pirate."));
+
+// Cleanup by agent name removes both agent versions created.
+await aiProjectClient.Agents.DeleteAgentAsync(existingJokerAgent.Name);
@@ -0,0 +1,40 @@
+# Creating and Managing AI Agents with Versioning
+
+This sample demonstrates how to create and manage AI agents with Azure Foundry Agents, including:
+- Creating agents with different versions
+- Retrieving agents by version or latest version
+- Running multi-turn conversations with agents
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+```
+
+## Run the sample
+
+Navigate to the FoundryAgents sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents
+dotnet run --project .\FoundryAgents_Step01.1_Basics
+```
+
+## What this sample demonstrates
+
+1. **Creating agents with versions**: Shows how to create multiple versions of the same agent with different instructions
+2. **Retrieving agents**: Demonstrates retrieving agents by specific version or getting the latest version
+3. **Multi-turn conversations**: Shows how to use threads to maintain conversation context across multiple agent runs
+4. **Agent cleanup**: Demonstrates proper resource cleanup by deleting agents
@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">

  <PropertyGroup>
    <OutputType>Exe</OutputType>
@@ -0,0 +1,39 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create and use a simple AI agent with Azure Foundry Agents as the backend.
+
+using Azure.AI.Projects;
+using Azure.AI.Projects.Agents;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+const string JokerInstructions = "You are good at telling jokes.";
+const string JokerName = "JokerAgent";
+
+// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Define the agent you want to create. (Prompt Agent in this case)
+AgentVersionCreationOptions options = new(new PromptAgentDefinition(model: deploymentName) { Instructions = JokerInstructions });
+
+// Azure.AI.Agents SDK creates and manages agent by name and versions.
+// You can create a server side agent version with the Azure.AI.Agents SDK client below.
+AgentVersion agentVersion = aiProjectClient.Agents.CreateAgentVersion(agentName: JokerName, options);
+
+// You can use an AIAgent with an already created server side agent version.
+AIAgent jokerAgent = aiProjectClient.AsAIAgent(agentVersion);
+
+// Invoke the agent with streaming support.
+await foreach (AgentResponseUpdate update in jokerAgent.RunStreamingAsync("Tell me a joke about a pirate."))
+{
+    Console.WriteLine(update);
+}
+
+// Cleanup by agent name removes the agent version created.
+await aiProjectClient.Agents.DeleteAgentAsync(jokerAgent.Name);
@@ -0,0 +1,46 @@
+# Running a Simple AI Agent with Streaming
+
+This sample demonstrates how to create and run a simple AI agent with Azure Foundry Agents, including both text and streaming responses.
+
+## What this sample demonstrates
+
+- Creating a simple AI agent with instructions
+- Running an agent with text output
+- Running an agent with streaming output
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+```
+
+## Run the sample
+
+Navigate to the FoundryAgents sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents
+dotnet run --project .\FoundryAgents_Step01.2_Running
+```
+
+## Expected behavior
+
+The sample will:
+
+1. Create an agent named "JokerAgent" with instructions to tell jokes
+2. Run the agent with a text prompt and display the response
+3. Run the agent again with streaming to display the response as it's generated
+4. Clean up resources by deleting the agent
+
@@ -9,6 +9,7 @@
  </PropertyGroup>

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

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

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

@@ -0,0 +1,54 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use an agent with function tools.
+// It shows both non-streaming and streaming agent interactions using weather-related tools.
+
+using System.ComponentModel;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+[Description("Get the weather for a given location.")]
+static string GetWeather([Description("The location to get the weather for.")] string location)
+    => $"The weather in {location} is cloudy with a high of 15°C.";
+
+const string AssistantInstructions = "You are a helpful assistant that can get weather information.";
+const string AssistantName = "WeatherAssistant";
+
+// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Define the agent with function tools.
+AITool tool = AIFunctionFactory.Create(GetWeather);
+
+// Create AIAgent directly
+var newAgent = await aiProjectClient.CreateAIAgentAsync(name: AssistantName, model: deploymentName, instructions: AssistantInstructions, tools: [tool]);
+
+// Getting an already existing agent by name with tools.
+/* 
+ * IMPORTANT: Since agents that are stored in the server only know the definition of the function tools (JSON Schema),
+ * you need to provided all invocable function tools when retrieving the agent so it can invoke them automatically.
+ * If no invocable tools are provided, the function calling needs to handled manually.
+ */
+var existingAgent = await aiProjectClient.GetAIAgentAsync(name: AssistantName, tools: [tool]);
+
+// Non-streaming agent interaction with function tools.
+AgentSession session = await existingAgent.CreateSessionAsync();
+Console.WriteLine(await existingAgent.RunAsync("What is the weather like in Amsterdam?", session));
+
+// Streaming agent interaction with function tools.
+session = await existingAgent.CreateSessionAsync();
+await foreach (AgentResponseUpdate update in existingAgent.RunStreamingAsync("What is the weather like in Amsterdam?", session))
+{
+    Console.WriteLine(update);
+}
+
+// Cleanup by agent name removes the agent version created.
+await aiProjectClient.Agents.DeleteAgentAsync(existingAgent.Name);
@@ -0,0 +1,48 @@
+# Using Function Tools with AI Agents
+
+This sample demonstrates how to use function tools with AI agents, allowing agents to call custom functions to retrieve information.
+
+## What this sample demonstrates
+
+- Creating function tools using AIFunctionFactory
+- Passing function tools to an AI agent
+- Running agents with function tools (text output)
+- Running agents with function tools (streaming output)
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+```
+
+## Run the sample
+
+Navigate to the FoundryAgents sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents
+dotnet run --project .\FoundryAgents_Step03.1_UsingFunctionTools
+```
+
+## Expected behavior
+
+The sample will:
+
+1. Create an agent named "WeatherAssistant" with a GetWeather function tool
+2. Run the agent with a text prompt asking about weather
+3. The agent will invoke the GetWeather function tool to retrieve weather information
+4. Run the agent again with streaming to display the response as it's generated
+5. Clean up resources by deleting the agent
+
@@ -9,6 +9,7 @@
  </PropertyGroup>

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

@@ -1,6 +1,9 @@
 // Copyright (c) Microsoft. All rights reserved.

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

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

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

-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 can get weather information.";
+const string AssistantName = "WeatherAssistant";

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

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

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

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

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

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

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

 Console.WriteLine($"\nAgent: {response}");
+
+// Cleanup by agent name removes the agent version created.
+await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
@@ -0,0 +1,51 @@
+# Using Function Tools with Approvals (Human-in-the-Loop)
+
+This sample demonstrates how to use function tools that require human approval before execution, implementing a human-in-the-loop workflow.
+
+## What this sample demonstrates
+
+- Creating approval-required function tools using ApprovalRequiredAIFunction
+- Handling user input requests for function approvals
+- Implementing human-in-the-loop approval workflows
+- Processing agent responses with pending approvals
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+```
+
+## Run the sample
+
+Navigate to the FoundryAgents sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents
+dotnet run --project .\FoundryAgents_Step04_UsingFunctionToolsWithApprovals
+```
+
+## Expected behavior
+
+The sample will:
+
+1. Create an agent named "WeatherAssistant" with an approval-required GetWeather function tool
+2. Run the agent with a prompt asking about weather
+3. The agent will request approval before invoking the GetWeather function
+4. The sample will prompt the user to approve or deny the function call (enter 'Y' to approve)
+5. After approval, the function will be executed and the result returned to the agent
+6. Clean up resources by deleting the agent
+
+**Note**: For hosted agents with remote users, combine this sample with the Persisted Conversations sample to persist chat history while waiting for user approval.
+
@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.Projects" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -15,23 +15,29 @@ using SampleApp;
 string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
 string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";

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

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

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

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

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

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

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

+// Cleanup by agent name removes the agent version created.
+await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
+
 namespace SampleApp
 {
    /// <summary>
-    /// Represents information about a person.
+    /// Represents information about a person, including their name, age, and occupation, matched to the JSON schema used in the agent.
    /// </summary>
    [Description("Information about a person including their name, age, and occupation")]
    public class PersonInfo
@@ -0,0 +1,49 @@
+# Structured Output with AI Agents
+
+This sample demonstrates how to configure AI agents to produce structured output in JSON format using JSON schemas.
+
+## What this sample demonstrates
+
+- Configuring agents with JSON schema response formats
+- Using generic RunAsync<T> method for structured output
+- Deserializing structured responses into typed objects
+- Running agents with streaming and structured output
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+```
+
+## Run the sample
+
+Navigate to the FoundryAgents sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents
+dotnet run --project .\FoundryAgents_Step05_StructuredOutput
+```
+
+## Expected behavior
+
+The sample will:
+
+1. Create an agent named "StructuredOutputAssistant" configured to produce JSON output
+2. Run the agent with a prompt to extract person information
+3. Deserialize the JSON response into a PersonInfo object
+4. Display the structured data (Name, Age, Occupation)
+5. Run the agent again with streaming and deserialize the streamed JSON response
+6. Clean up resources by deleting the agent
+
@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.Projects" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -1,6 +1,6 @@
 // Copyright (c) Microsoft. All rights reserved.

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

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

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

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

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

 // Run the agent again with the resumed session.
 Console.WriteLine(await agent.RunAsync("Now tell the same joke in the voice of a pirate, and add some emojis to the joke.", resumedSession));
+
+// Cleanup by agent name removes the agent version created.
+await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
@@ -0,0 +1,50 @@
+# Persisted Conversations with AI Agents
+
+This sample demonstrates how to serialize and persist agent conversation threads to storage, allowing conversations to be resumed later.
+
+## What this sample demonstrates
+
+- Serializing agent threads to JSON
+- Persisting thread state to disk
+- Loading and deserializing thread state from storage
+- Resuming conversations with persisted threads
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+```
+
+## Run the sample
+
+Navigate to the FoundryAgents sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents
+dotnet run --project .\FoundryAgents_Step06_PersistedConversations
+```
+
+## Expected behavior
+
+The sample will:
+
+1. Create an agent named "JokerAgent" with instructions to tell jokes
+2. Create a thread and run the agent with an initial prompt
+3. Serialize the thread state to JSON
+4. Save the serialized thread to a temporary file
+5. Load the thread from the file and deserialize it
+6. Resume the conversation with the same thread using a follow-up prompt
+7. Clean up resources by deleting the agent
+
@@ -9,6 +9,8 @@
  </PropertyGroup>

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

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

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

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

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

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

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

-Console.WriteLine();
+// Cleanup by agent name removes the agent version created.
+await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
@@ -0,0 +1,51 @@
+# Observability with OpenTelemetry
+
+This sample demonstrates how to add observability to AI agents using OpenTelemetry for tracing and monitoring.
+
+## What this sample demonstrates
+
+- Setting up OpenTelemetry TracerProvider
+- Configuring console exporter for telemetry output
+- Configuring Azure Monitor exporter for Application Insights
+- Adding OpenTelemetry middleware to agents
+- Running agents with telemetry collection (text and streaming)
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+- (Optional) Application Insights connection string for Azure Monitor integration
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_AI_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$env:APPLICATIONINSIGHTS_CONNECTION_STRING="your-connection-string"  # Optional, for Azure Monitor integration
+```
+
+## Run the sample
+
+Navigate to the FoundryAgents sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/FoundryAgents
+dotnet run --project .\FoundryAgents_Step07_Observability
+```
+
+## Expected behavior
+
+The sample will:
+
+1. Create a TracerProvider with console exporter (and optionally Azure Monitor exporter)
+2. Create an agent named "JokerAgent" with OpenTelemetry middleware
+3. Run the agent with a text prompt and display telemetry traces to console
+4. Run the agent again with streaming and display telemetry traces
+5. Clean up resources by deleting the agent
+
@@ -11,6 +11,8 @@
  </PropertyGroup>

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

@@ -0,0 +1,97 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to use dependency injection to register an AIAgent and use it from a hosted service with a user input chat loop.
+
+using System.ClientModel;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+const string JokerInstructions = "You are good at telling jokes.";
+const string JokerName = "JokerAgent";
+
+// 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 new agent if one doesn't exist already.
+ChatClientAgent agent;
+try
+{
+    agent = await aIProjectClient.GetAIAgentAsync(name: JokerName);
+}
+catch (ClientResultException ex) when (ex.Status == 404)
+{
+    agent = await aIProjectClient.CreateAIAgentAsync(name: JokerName, model: deploymentName, instructions: JokerInstructions);
+}
+
+// Create a host builder that we will register services with and then run.
+HostApplicationBuilder builder = Host.CreateApplicationBuilder(args);
+
+// Add the agents client to the service collection.
+builder.Services.AddSingleton((sp) => aIProjectClient);
+
+// Add the AI agent to the service collection.
+builder.Services.AddSingleton<AIAgent>((sp) => 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);
+
+/// <summary>
+/// A sample service that uses an AI agent to respond to user input.
+/// </summary>
+internal sealed class SampleService(AIProjectClient client, AIAgent agent, IHostApplicationLifetime appLifetime) : IHostedService
+{
+    private AgentSession? _session;
+
+    public async Task StartAsync(CancellationToken cancellationToken)
+    {
+        // Create a session that will be used for the entirety of the service lifetime so that the user can ask follow up questions.
+        this._session = await agent.CreateSessionAsync(cancellationToken);
+        _ = this.RunAsync(appLifetime.ApplicationStopping);
+    }
+
+    public async Task RunAsync(CancellationToken cancellationToken)
+    {
+        // Delay a little to allow the service to finish starting.
+        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 the user enters no input, signal the application to shut down.
+            if (string.IsNullOrWhiteSpace(input))
+            {
+                appLifetime.StopApplication();
+                break;
+            }
+
+            // Stream the output to the console as it is generated.
+            await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(input, this._session, cancellationToken: cancellationToken))
+            {
+                Console.Write(update);
+            }
+
+            Console.WriteLine();
+        }
+    }
+
+    public async Task StopAsync(CancellationToken cancellationToken)
+    {
+        Console.WriteLine("\nDeleting agent ...");
+        await client.Agents.DeleteAgentAsync(agent.Name, cancellationToken).ConfigureAwait(false);
+    }
+}
--- 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