Merge branch 'feature-foundry-agents' of https://github.com/microsoft/agent-framework into feature-foundry-agents

Package descriptions
Merge branch 'main' into feature-foundry-agents
2026-06-16 21:04:09 +08:00 · 2025-11-15 01:05:10 +00:00 · 2025-11-15 01:05:00 +00:00 · 2025-11-15 01:04:31 +00:00 · 2025-11-15 00:42:41 +00:00 · 2025-11-14 15:44:00 -08:00
631 changed files with 44862 additions and 7923 deletions
@@ -0,0 +1,4 @@
+# Code ownership assignments
+# https://docs.github.com/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners
+
+python/packages/azurefunctions/ @microsoft/agentframework-durabletask-developers
@@ -0,0 +1,36 @@
+name: Azure Functions Integration Test Setup
+description: Prepare local emulators and tools for Azure Functions integration tests
+
+runs:
+    using: "composite"
+    steps:
+      - name: Start Durable Task Scheduler Emulator
+        shell: bash
+        run: |
+          if [ "$(docker ps -aq -f name=dts-emulator)" ]; then
+            echo "Stopping and removing existing Durable Task Scheduler Emulator"
+            docker rm -f dts-emulator
+          fi
+          echo "Starting Durable Task Scheduler Emulator"
+          docker run -d --name dts-emulator -p 8080:8080 -p 8082:8082 mcr.microsoft.com/dts/dts-emulator:latest
+          echo "Waiting for Durable Task Scheduler Emulator to be ready"
+          timeout 30 bash -c 'until curl --silent http://localhost:8080/healthz; do sleep 1; done'
+          echo "Durable Task Scheduler Emulator is ready"
+      - name: Start Azurite (Azure Storage emulator)
+        shell: bash
+        run: |
+          if [ "$(docker ps -aq -f name=azurite)" ]; then
+            echo "Stopping and removing existing Azurite (Azure Storage emulator)"
+            docker rm -f azurite
+          fi
+          echo "Starting Azurite (Azure Storage emulator)"
+          docker run -d --name azurite -p 10000:10000 -p 10001:10001 -p 10002:10002 mcr.microsoft.com/azure-storage/azurite
+          echo "Waiting for Azurite (Azure Storage emulator) to be ready"
+          timeout 30 bash -c 'until curl --silent http://localhost:10000/devstoreaccount1; do sleep 1; done'
+          echo "Azurite (Azure Storage emulator) is ready"
+      - name: Install Azure Functions Core Tools
+        shell: bash
+        run: |
+          echo "Installing Azure Functions Core Tools"
+          npm install -g azure-functions-core-tools@4 --unsafe-perm true
+          func --version
@@ -151,6 +151,14 @@ jobs:
          tenant-id: ${{ secrets.AZURE_TENANT_ID }}
          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}

+        # This setup action is required for both Durable Task and Azure Functions integration tests.
+        # We only run it on Ubuntu since the Durable Task and Azure Functions features are not available
+        # on .NET Framework (net472) which is what we use the Windows runner for.
+      - name: Set up Durable Task and Azure Functions Integration Test Emulators
+        if: github.event_name != 'pull_request' && matrix.integration-tests && matrix.os == 'ubuntu-latest'
+        uses: ./.github/actions/azure-functions-integration-setup
+        id: azure-functions-setup
+
      - name: Run Integration Tests
        shell: bash
        if: github.event_name != 'pull_request' && matrix.integration-tests
@@ -172,6 +180,9 @@ jobs:
          OpenAI__ApiKey: ${{ secrets.OPENAI__APIKEY }}
          OpenAI__ChatModelId: ${{ vars.OPENAI__CHATMODELID }}
          OpenAI__ChatReasoningModelId: ${{ vars.OPENAI__CHATREASONINGMODELID }}
+          # Azure OpenAI Models
+          AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__CHATDEPLOYMENTNAME }}
+          AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
          # Azure AI Foundry
          AzureAI__Endpoint: ${{ secrets.AZUREAI__ENDPOINT }}
          AzureAI__DeploymentName: ${{ vars.AZUREAI__DEPLOYMENTNAME }}
@@ -28,6 +28,8 @@ jobs:
      UV_PYTHON: ${{ matrix.python-version }}
    steps:
      - uses: actions/checkout@v5
+        with:
+          fetch-depth: 0
      - name: Set up python and install the project
        id: python-setup
        uses: ./.github/actions/python-setup
@@ -46,4 +48,6 @@ jobs:
        with:
          extra_args: --config python/.pre-commit-config.yaml --all-files
      - name: Run Mypy
-        run: uv run poe mypy
+        env:
+          GITHUB_BASE_REF: ${{ github.event.pull_request.base.ref || github.base_ref || 'main' }}
+        run: uv run poe ci-mypy
@@ -66,6 +66,11 @@ jobs:
      AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: ${{ vars.AZUREOPENAI__RESPONSESDEPLOYMENTNAME }}
      AZURE_OPENAI_ENDPOINT: ${{ vars.AZUREOPENAI__ENDPOINT }}
      LOCAL_MCP_URL: ${{ vars.LOCAL_MCP__URL }}
+      # For Azure Functions integration tests
+      FUNCTIONS_WORKER_RUNTIME: "python"
+      DURABLE_TASK_SCHEDULER_CONNECTION_STRING: "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None"
+      AzureWebJobsStorage: "UseDevelopmentStorage=true"
+
    defaults:
      run:
        working-directory: python
@@ -87,6 +92,9 @@ jobs:
          client-id: ${{ secrets.AZURE_CLIENT_ID }}
          tenant-id: ${{ secrets.AZURE_TENANT_ID }}
          subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+      - name: Set up Azure Functions Integration Test Emulators
+        uses: ./.github/actions/azure-functions-integration-setup
+        id: azure-functions-setup
      - name: Test with pytest
        timeout-minutes: 10
        run: uv run poe all-tests -n logical --dist loadfile --dist worksteal --timeout 300 --retries 3 --retry-delay 10
@@ -205,10 +205,22 @@ agents.md
 .claude/
 WARP.md

+# Azurite storage emulator files
+*/__azurite_db_blob__.json
+*/__azurite_db_blob_extent__.json
+*/__azurite_db_queue__.json
+*/__azurite_db_queue_extent__.json
+*/__azurite_db_table__.json
+*/__blobstorage__/
+*/__queuestorage__/
+
+# Azure Functions local settings
+local.settings.json
+
 # Frontend
 **/frontend/node_modules/
 **/frontend/.vite/
 **/frontend/dist/

 # Database files
-*.db
+*.db
@@ -17,6 +17,8 @@
    <PackageVersion Include="Aspire.Microsoft.Azure.Cosmos" Version="$(AspireAppHostSdkVersion)" />
    <PackageVersion Include="CommunityToolkit.Aspire.OllamaSharp" Version="13.0.0-beta.435" />
    <!-- Azure.* -->
+    <PackageVersion Include="Azure.AI.Projects" Version="1.2.0-beta.1" />
+    <PackageVersion Include="Azure.AI.Projects.OpenAI" Version="1.0.0-beta.1" />
    <PackageVersion Include="Azure.AI.Agents.Persistent" Version="1.2.0-beta.7" />
    <PackageVersion Include="Azure.AI.OpenAI" Version="2.5.0-beta.1" />
    <PackageVersion Include="Azure.Identity" Version="1.17.0" />
@@ -93,10 +95,24 @@
    <!-- Identity -->
    <PackageVersion Include="Microsoft.Identity.Client.Extensions.Msal" Version="4.78.0" />
    <!-- Workflows -->
-    <PackageVersion Include="Microsoft.Bot.ObjectModel" Version="1.2025.1003.2" />
-    <PackageVersion Include="Microsoft.Bot.ObjectModel.Json" Version="1.2025.1003.2" />
-    <PackageVersion Include="Microsoft.Bot.ObjectModel.PowerFx" Version="1.2025.1003.2" />
-    <PackageVersion Include="Microsoft.PowerFx.Interpreter" Version="1.4.0" />
+    <PackageVersion Include="Microsoft.Bot.ObjectModel" Version="1.2025.1106.1" />
+    <PackageVersion Include="Microsoft.Bot.ObjectModel.Json" Version="1.2025.1106.1" />
+    <PackageVersion Include="Microsoft.Bot.ObjectModel.PowerFx" Version="1.2025.1106.1" />
+    <PackageVersion Include="Microsoft.PowerFx.Interpreter" Version="1.5.0-build.20251008-1002" />
+    <!-- Durable Task -->
+    <PackageVersion Include="Microsoft.DurableTask.Client" Version="1.16.2" />
+    <PackageVersion Include="Microsoft.DurableTask.Client.AzureManaged" Version="1.16.2-preview.1" />
+    <PackageVersion Include="Microsoft.DurableTask.Worker" Version="1.16.2" />
+    <PackageVersion Include="Microsoft.DurableTask.Worker.AzureManaged" Version="1.16.2-preview.1" />
+    <!-- Azure Functions -->
+    <PackageVersion Include="Microsoft.Azure.Functions.Worker" Version="2.50.0" />
+    <PackageVersion Include="Microsoft.Azure.Functions.Worker.ApplicationInsights" Version="2.50.0" />
+    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" Version="1.9.0" />
+    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" Version="1.0.0" />
+    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.Http" Version="3.3.0" />
+    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" Version="2.1.0" />
+    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Extensions.Mcp" Version="1.0.0" />
+    <PackageVersion Include="Microsoft.Azure.Functions.Worker.Sdk" Version="2.0.7" />
    <!-- Community -->
    <PackageVersion Include="System.Linq.Async" Version="6.0.3" />
    <!-- Test -->
@@ -104,8 +120,6 @@
    <PackageVersion Include="Microsoft.AspNetCore.TestHost" Version="9.0.11" />
    <PackageVersion Include="Microsoft.NET.Test.Sdk" Version="18.0.0" />
    <PackageVersion Include="Moq" Version="[4.18.4]" />
-    <PackageVersion Include="Microsoft.SemanticKernel.Agents.Abstractions" Version="1.67.0" />
-    <PackageVersion Include="Microsoft.SemanticKernel.Agents.Yaml" Version="1.67.0-beta" />
    <PackageVersion Include="xunit" Version="2.9.3" />
    <PackageVersion Include="xunit.abstractions" Version="2.0.3" />
    <PackageVersion Include="xunit.runner.visualstudio" Version="3.1.3" />
@@ -151,4 +165,4 @@
      <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
    </PackageReference>
  </ItemGroup>
-</Project>
+</Project>
@@ -23,6 +23,17 @@
    <Project Path="samples/AGUIClientServer/AGUIDojoServer/AGUIDojoServer.csproj" />
    <Project Path="samples/AGUIClientServer/AGUIServer/AGUIServer.csproj" />
  </Folder>
+  <Folder Name="/Samples/AzureFunctions/">
+    <File Path="samples/AzureFunctions/.editorconfig" />
+    <File Path="samples/AzureFunctions/README.md" />
+    <Project Path="samples/AzureFunctions/01_SingleAgent/01_SingleAgent.csproj" />
+    <Project Path="samples/AzureFunctions/02_AgentOrchestration_Chaining/02_AgentOrchestration_Chaining.csproj" />
+    <Project Path="samples/AzureFunctions/03_AgentOrchestration_Concurrency/03_AgentOrchestration_Concurrency.csproj" />
+    <Project Path="samples/AzureFunctions/04_AgentOrchestration_Conditionals/04_AgentOrchestration_Conditionals.csproj" />
+    <Project Path="samples/AzureFunctions/05_AgentOrchestration_HITL/05_AgentOrchestration_HITL.csproj" />
+    <Project Path="samples/AzureFunctions/06_LongRunningTools/06_LongRunningTools.csproj" />
+    <Project Path="samples/AzureFunctions/07_AgentAsMcpTool/07_AgentAsMcpTool.csproj" />
+  </Folder>
  <Folder Name="/Samples/GettingStarted/">
    <File Path="samples/GettingStarted/README.md" />
  </Folder>
@@ -33,7 +44,8 @@
  <Folder Name="/Samples/GettingStarted/AgentProviders/">
    <File Path="samples/GettingStarted/AgentProviders/README.md" />
    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_A2A/Agent_With_A2A.csproj" />
-    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureFoundryAgent/Agent_With_AzureFoundryAgent.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIAgentsPersistent/Agent_With_AzureAIAgentsPersistent.csproj" />
+    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureAIProject/Agent_With_AzureAIProject.csproj" />
    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureFoundryModel/Agent_With_AzureFoundryModel.csproj" />
    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureOpenAIChatCompletion/Agent_With_AzureOpenAIChatCompletion.csproj" />
    <Project Path="samples/GettingStarted/AgentProviders/Agent_With_AzureOpenAIResponses/Agent_With_AzureOpenAIResponses.csproj" />
@@ -80,12 +92,35 @@
    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step01_Running/Agent_OpenAI_Step01_Running.csproj" />
    <Project Path="samples/GettingStarted/AgentWithOpenAI/Agent_OpenAI_Step02_Reasoning/Agent_OpenAI_Step02_Reasoning.csproj" />
  </Folder>
+  <Folder Name="/Samples/Purview/" />
+  <Folder Name="/Samples/Purview/AgentWithPurview/">
+    <Project Path="samples/Purview/AgentWithPurview/AgentWithPurview.csproj" />
+  </Folder>
  <Folder Name="/Samples/GettingStarted/AgentWithRAG/">
    <File Path="samples/GettingStarted/AgentWithRAG/README.md" />
    <Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step01_BasicTextRAG/AgentWithRAG_Step01_BasicTextRAG.csproj" />
    <Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step02_CustomVectorStoreRAG/AgentWithRAG_Step02_CustomVectorStoreRAG.csproj" />
    <Project Path="samples/GettingStarted/AgentWithRAG/AgentWithRAG_Step03_CustomRAGDataSource/AgentWithRAG_Step03_CustomRAGDataSource.csproj" />
  </Folder>
+  <Folder Name="/Samples/GettingStarted/FoundryAgents/">
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step01.1_Basics/FoundryAgents_Step01.1_Basics.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step01.2_Running/FoundryAgents_Step01.2_Running.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step02_MultiturnConversation/FoundryAgents_Step02_MultiturnConversation.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.1_UsingFunctionTools/FoundryAgents_Step03.1_UsingFunctionTools.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI/FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step04_UsingFunctionToolsWithApprovals/FoundryAgents_Step04_UsingFunctionToolsWithApprovals.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step05_StructuredOutput/FoundryAgents_Step05_StructuredOutput.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step06_PersistedConversations/FoundryAgents_Step06_PersistedConversations.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step07_Observability/FoundryAgents_Step07_Observability.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step08_DependencyInjection/FoundryAgents_Step08_DependencyInjection.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step09_UsingMcpClientAsTools/FoundryAgents_Step09_UsingMcpClientAsTools.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step10_UsingImages/FoundryAgents_Step10_UsingImages.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step11_AsFunctionTool/FoundryAgents_Step11_AsFunctionTool.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step12_Middleware/FoundryAgents_Step12_Middleware.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step13_Plugins/FoundryAgents_Step13_Plugins.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step14_CodeInterpreter/FoundryAgents_Step14_CodeInterpreter.csproj" />
+    <Project Path="samples/GettingStarted/FoundryAgents/FoundryAgents_Step15_ComputerUse/FoundryAgents_Step15_ComputerUse.csproj" />
+  </Folder>
  <Folder Name="/Samples/GettingStarted/ModelContextProtocol/">
    <File Path="samples/GettingStarted/ModelContextProtocol/README.md" />
    <Project Path="samples/GettingStarted/ModelContextProtocol/Agent_MCP_Server/Agent_MCP_Server.csproj" />
@@ -110,13 +145,22 @@
  </Folder>
  <Folder Name="/Samples/GettingStarted/Workflows/Declarative/">
    <File Path="samples/GettingStarted/Workflows/Declarative/README.md" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/ConfirmInput/ConfirmInput.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/CustomerSupport/CustomerSupport.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/DeepResearch/DeepResearch.csproj" />
    <Project Path="samples/GettingStarted/Workflows/Declarative/ExecuteCode/ExecuteCode.csproj" />
    <Project Path="samples/GettingStarted/Workflows/Declarative/ExecuteWorkflow/ExecuteWorkflow.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/FunctionTools/FunctionTools.csproj" />
    <Project Path="samples/GettingStarted/Workflows/Declarative/GenerateCode/GenerateCode.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/HostedWorkflow/HostedWorkflow.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/InputArguments/InputArguments.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/Marketing/Marketing.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/StudentTeacher/StudentTeacher.csproj" />
+    <Project Path="samples/GettingStarted/Workflows/Declarative/ToolApproval/ToolApproval.csproj" />
  </Folder>
  <Folder Name="/Samples/GettingStarted/Workflows/Declarative/Examples/">
+    <File Path="../workflow-samples/ConfirmInput.yaml" />
    <File Path="../workflow-samples/DeepResearch.yaml" />
-    <File Path="../workflow-samples/HumanInLoop.yaml" />
    <File Path="../workflow-samples/Marketing.yaml" />
    <File Path="../workflow-samples/MathChat.yaml" />
    <File Path="../workflow-samples/README.md" />
@@ -161,8 +205,8 @@
  </Folder>
  <Folder Name="/Samples/HostedAgents/">
    <Project Path="samples/HostedAgents/AgentsInWorkflows/AgentsInWorkflows.csproj" />
-    <Project Path="samples/HostedAgents/AgentWithTextSearchRag/AgentWithTextSearchRag.csproj" />
    <Project Path="samples/HostedAgents/AgentWithHostedMCP/AgentWithHostedMCP.csproj" />
+    <Project Path="samples/HostedAgents/AgentWithTextSearchRag/AgentWithTextSearchRag.csproj" />
  </Folder>
  <Folder Name="/Solution Items/">
    <File Path=".editorconfig" />
@@ -288,15 +332,20 @@
    <Project Path="src/Microsoft.Agents.AI.Abstractions/Microsoft.Agents.AI.Abstractions.csproj" />
    <Project Path="src/Microsoft.Agents.AI.AGUI/Microsoft.Agents.AI.AGUI.csproj" />
    <Project Path="src/Microsoft.Agents.AI.AzureAI.Persistent/Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.AzureAI/Microsoft.Agents.AI.AzureAI.csproj" />
    <Project Path="src/Microsoft.Agents.AI.CopilotStudio/Microsoft.Agents.AI.CopilotStudio.csproj" />
    <Project Path="src/Microsoft.Agents.AI.DevUI/Microsoft.Agents.AI.DevUI.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.DurableTask/Microsoft.Agents.AI.DurableTask.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting.A2A.AspNetCore/Microsoft.Agents.AI.Hosting.A2A.AspNetCore.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting.A2A/Microsoft.Agents.AI.Hosting.A2A.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.Hosting.AzureFunctions/Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting.OpenAI/Microsoft.Agents.AI.Hosting.OpenAI.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Hosting/Microsoft.Agents.AI.Hosting.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Mem0/Microsoft.Agents.AI.Mem0.csproj" />
    <Project Path="src/Microsoft.Agents.AI.OpenAI/Microsoft.Agents.AI.OpenAI.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.Purview/Microsoft.Agents.AI.Purview.csproj" />
+    <Project Path="src/Microsoft.Agents.AI.Workflows.Declarative.AzureAI/Microsoft.Agents.AI.Workflows.Declarative.AzureAI.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Workflows.Declarative/Microsoft.Agents.AI.Workflows.Declarative.csproj" />
    <Project Path="src/Microsoft.Agents.AI.Workflows/Microsoft.Agents.AI.Workflows.csproj" />
    <Project Path="src/Microsoft.Agents.AI/Microsoft.Agents.AI.csproj" />
@@ -304,9 +353,12 @@
  <Folder Name="/Tests/" />
  <Folder Name="/Tests/IntegrationTests/">
    <Project Path="tests/AgentConformance.IntegrationTests/AgentConformance.IntegrationTests.csproj" />
+    <Project Path="tests/AzureAI.IntegrationTests/AzureAI.IntegrationTests.csproj" />
    <Project Path="tests/AzureAIAgentsPersistent.IntegrationTests/AzureAIAgentsPersistent.IntegrationTests.csproj" />
    <Project Path="tests/CopilotStudio.IntegrationTests/CopilotStudio.IntegrationTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.DurableTask.IntegrationTests/Microsoft.Agents.AI.DurableTask.IntegrationTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.IntegrationTests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.IntegrationTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests/Microsoft.Agents.AI.Hosting.AzureFunctions.IntegrationTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Mem0.IntegrationTests/Microsoft.Agents.AI.Mem0.IntegrationTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Workflows.Declarative.IntegrationTests/Microsoft.Agents.AI.Workflows.Declarative.IntegrationTests.csproj" />
    <Project Path="tests/OpenAIAssistant.IntegrationTests/OpenAIAssistant.IntegrationTests.csproj" />
@@ -318,12 +370,16 @@
    <Project Path="tests/Microsoft.Agents.AI.Abstractions.UnitTests/Microsoft.Agents.AI.Abstractions.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.AGUI.UnitTests/Microsoft.Agents.AI.AGUI.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.AzureAI.Persistent.UnitTests/Microsoft.Agents.AI.AzureAI.Persistent.UnitTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.AzureAI.UnitTests/Microsoft.Agents.AI.AzureAI.UnitTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.DurableTask.UnitTests/Microsoft.Agents.AI.DurableTask.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Hosting.A2A.UnitTests/Microsoft.Agents.AI.Hosting.A2A.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.UnitTests/Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.UnitTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.Hosting.AzureFunctions.UnitTests/Microsoft.Agents.AI.Hosting.AzureFunctions.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Hosting.OpenAI.UnitTests/Microsoft.Agents.AI.Hosting.OpenAI.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Hosting.UnitTests/Microsoft.Agents.AI.Hosting.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Mem0.UnitTests/Microsoft.Agents.AI.Mem0.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.OpenAI.UnitTests/Microsoft.Agents.AI.OpenAI.UnitTests.csproj" />
+    <Project Path="tests/Microsoft.Agents.AI.Purview.UnitTests/Microsoft.Agents.AI.Purview.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.UnitTests/Microsoft.Agents.AI.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests/Microsoft.Agents.AI.Workflows.Declarative.UnitTests.csproj" />
    <Project Path="tests/Microsoft.Agents.AI.Workflows.UnitTests/Microsoft.Agents.AI.Workflows.UnitTests.csproj" />
@@ -11,4 +11,13 @@
  <ItemGroup Condition="'$(InjectSharedBuildTestCode)' == 'true'">
    <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\CodeTests\*.cs" LinkBase="Shared\CodeTests" />
  </ItemGroup>
+  <ItemGroup Condition="'$(InjectSharedWorkflowsExecution)' == 'true'">
+    <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\Workflows\Execution\*.cs" LinkBase="Shared\Workflows" />
+  </ItemGroup>
+  <ItemGroup Condition="'$(InjectSharedWorkflowsSettings)' == 'true'">
+    <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\Workflows\Settings\*.cs" LinkBase="Shared\Workflows" />
+  </ItemGroup>
+  <ItemGroup Condition="'$(InjectSharedFoundryAgents)' == 'true'">
+    <Compile Include="$(MSBuildThisFileDirectory)\..\..\src\Shared\Foundry\Agents\*.cs" LinkBase="Shared\Foundry" />
+  </ItemGroup>
 </Project>
@@ -2,9 +2,9 @@
  <PropertyGroup>
    <!-- Central version prefix - applies to all nuget packages. -->
    <VersionPrefix>1.0.0</VersionPrefix>
-    <PackageVersion Condition="'$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).251110.2</PackageVersion>
-    <PackageVersion Condition="'$(VersionSuffix)' == ''">$(VersionPrefix)-preview.251110.2</PackageVersion>
-    <GitTag>1.0.0-preview.251110.2</GitTag>
+    <PackageVersion Condition="'$(VersionSuffix)' != ''">$(VersionPrefix)-$(VersionSuffix).251113.1</PackageVersion>
+    <PackageVersion Condition="'$(VersionSuffix)' == ''">$(VersionPrefix)-preview.251113.1</PackageVersion>
+    <GitTag>1.0.0-preview.251113.1</GitTag>

    <Configurations>Debug;Release;Publish</Configurations>
    <IsPackable>true</IsPackable>
@@ -0,0 +1,10 @@
+# .editorconfig
+[*.cs]
+
+# See https://github.com/Azure/azure-functions-durable-extension/issues/3173
+dotnet_diagnostic.DURABLE0001.severity = none
+dotnet_diagnostic.DURABLE0002.severity = none
+dotnet_diagnostic.DURABLE0003.severity = none
+dotnet_diagnostic.DURABLE0004.severity = none
+dotnet_diagnostic.DURABLE0005.severity = none
+dotnet_diagnostic.DURABLE0006.severity = none
@@ -0,0 +1,42 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFramework>net9.0</TargetFramework>
+    <AzureFunctionsVersion>v4</AzureFunctionsVersion>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <!-- The Functions build tools don't like namespaces that start with a number -->
+    <AssemblyName>SingleAgent</AssemblyName>
+    <RootNamespace>SingleAgent</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <FrameworkReference Include="Microsoft.AspNetCore.App" />
+  </ItemGroup>
+
+  <!-- Azure Functions packages -->
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Azure.Functions.Worker" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
+  <!--
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
+    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+</Project>
@@ -0,0 +1,37 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Hosting.AzureFunctions;
+using Microsoft.Azure.Functions.Worker.Builder;
+using Microsoft.Extensions.Hosting;
+using OpenAI;
+
+// Get the Azure OpenAI endpoint and deployment name from environment variables.
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
+
+// Set up an AI agent following the standard Microsoft Agent Framework pattern.
+const string JokerName = "Joker";
+const string JokerInstructions = "You are good at telling jokes.";
+
+AIAgent agent = client.GetChatClient(deploymentName).CreateAIAgent(JokerInstructions, JokerName);
+
+// Configure the function app to host the AI agent.
+// This will automatically generate HTTP API endpoints for the agent.
+using IHost app = FunctionsApplication
+    .CreateBuilder(args)
+    .ConfigureFunctionsWebApplication()
+    .ConfigureDurableAgents(options => options.AddAIAgent(agent))
+    .Build();
+app.Run();
@@ -0,0 +1,89 @@
+# Single Agent Sample
+
+This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a simple Azure Functions app that hosts a single AI agent and provides direct HTTP API access for interactive conversations.
+
+## Key Concepts Demonstrated
+
+- Using the Microsoft Agent Framework to define a simple AI agent with a name and instructions.
+- Registering agents with the Function app and running them using HTTP.
+- Conversation management (via session IDs) for isolated interactions.
+
+## Environment Setup
+
+See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
+
+## Running the Sample
+
+With the environment setup and function app running, you can test the sample by sending an HTTP request to the agent endpoint.
+
+You can use the `demo.http` file to send a message to the agent, or a command line tool like `curl` as shown below:
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl -X POST http://localhost:7071/api/agents/Joker/run \
+    -H "Content-Type: text/plain" \
+    -d "Tell me a joke about a pirate."
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/agents/Joker/run `
+    -ContentType text/plain `
+    -Body "Tell me a joke about a pirate."
+```
+
+You can also send JSON requests:
+
+```bash
+curl -X POST http://localhost:7071/api/agents/Joker/run \
+    -H "Content-Type: application/json" \
+    -H "Accept: application/json" \
+    -d '{"message": "Tell me a joke about a pirate."}'
+```
+
+To continue a conversation, include the `thread_id` in the query string or JSON body:
+
+```bash
+curl -X POST "http://localhost:7071/api/agents/Joker/run?thread_id=@dafx-joker@your-thread-id" \
+    -H "Content-Type: application/json" \
+    -H "Accept: application/json" \
+    -d '{"message": "Tell me another one."}'
+```
+
+The response from the agent will be displayed in the terminal where you ran `func start`. The expected `text/plain` output will look something like:
+
+```text
+Why don't pirates ever learn the alphabet? Because they always get stuck at "C"!
+```
+
+The expected `application/json` output will look something like:
+
+```json
+{
+  "status": 200,
+  "thread_id": "@dafx-joker@your-thread-id",
+  "response": {
+    "Messages": [
+      {
+        "AuthorName": "Joker",
+        "CreatedAt": "2025-11-11T12:00:00.0000000Z",
+        "Role": "assistant",
+        "Contents": [
+          {
+            "Type": "text",
+            "Text": "Why don't pirates ever learn the alphabet? Because they always get stuck at 'C'!"
+          }
+        ]
+      }
+    ],
+    "Usage": {
+      "InputTokenCount": 78,
+      "OutputTokenCount": 36,
+      "TotalTokenCount": 114
+    }
+  }
+}
+```
@@ -0,0 +1,8 @@
+# Default endpoint address for local testing
+@authority=http://localhost:7071
+
+### Prompt the agent
+POST {{authority}}/api/agents/Joker/run
+Content-Type: text/plain
+
+Tell me a joke about a pirate.
@@ -0,0 +1,20 @@
+{
+  "version": "2.0",
+  "logging": {
+    "logLevel": {
+      "Microsoft.Agents.AI.DurableTask": "Information",
+      "Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
+      "DurableTask": "Information",
+      "Microsoft.DurableTask": "Information"
+    }
+  },
+  "extensions": {
+    "durableTask": {
+      "hubName": "default",
+      "storageProvider": {
+        "type": "AzureManaged",
+        "connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
+      }
+    }
+  }
+}
@@ -0,0 +1,10 @@
+{
+  "IsEncrypted": false,
+  "Values": {
+    "FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
+    "AzureWebJobsStorage": "UseDevelopmentStorage=true",
+    "DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
+    "AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
+    "AZURE_OPENAI_DEPLOYMENT": "<AZURE_OPENAI_DEPLOYMENT>"
+  }
+}
@@ -0,0 +1,42 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFramework>net9.0</TargetFramework>
+    <AzureFunctionsVersion>v4</AzureFunctionsVersion>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <!-- The Functions build tools don't like namespaces that start with a number -->
+    <AssemblyName>AgentOrchestration_Chaining</AssemblyName>
+    <RootNamespace>AgentOrchestration_Chaining</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <FrameworkReference Include="Microsoft.AspNetCore.App" />
+  </ItemGroup>
+
+  <!-- Azure Functions packages -->
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Azure.Functions.Worker" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
+  <!--
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
+    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+</Project>
@@ -0,0 +1,92 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Net;
+using System.Text.Json;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.Azure.Functions.Worker;
+using Microsoft.Azure.Functions.Worker.Http;
+using Microsoft.DurableTask;
+using Microsoft.DurableTask.Client;
+
+namespace AgentOrchestration_Chaining;
+
+public static class FunctionTriggers
+{
+    public sealed record TextResponse(string Text);
+
+    [Function(nameof(RunOrchestrationAsync))]
+    public static async Task<string> RunOrchestrationAsync([OrchestrationTrigger] TaskOrchestrationContext context)
+    {
+        DurableAIAgent writer = context.GetAgent("WriterAgent");
+        AgentThread writerThread = writer.GetNewThread();
+
+        AgentRunResponse<TextResponse> initial = await writer.RunAsync<TextResponse>(
+            message: "Write a concise inspirational sentence about learning.",
+            thread: writerThread);
+
+        AgentRunResponse<TextResponse> refined = await writer.RunAsync<TextResponse>(
+            message: $"Improve this further while keeping it under 25 words: {initial.Result.Text}",
+            thread: writerThread);
+
+        return refined.Result.Text;
+    }
+
+    // POST /singleagent/run
+    [Function(nameof(StartOrchestrationAsync))]
+    public static async Task<HttpResponseData> StartOrchestrationAsync(
+        [HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = "singleagent/run")] HttpRequestData req,
+        [DurableClient] DurableTaskClient client)
+    {
+        string instanceId = await client.ScheduleNewOrchestrationInstanceAsync(
+            orchestratorName: nameof(RunOrchestrationAsync));
+
+        HttpResponseData response = req.CreateResponse(HttpStatusCode.Accepted);
+        await response.WriteAsJsonAsync(new
+        {
+            message = "Single-agent orchestration started.",
+            instanceId,
+            statusQueryGetUri = GetStatusQueryGetUri(req, instanceId),
+        });
+        return response;
+    }
+
+    // GET /singleagent/status/{instanceId}
+    [Function(nameof(GetOrchestrationStatusAsync))]
+    public static async Task<HttpResponseData> GetOrchestrationStatusAsync(
+        [HttpTrigger(AuthorizationLevel.Anonymous, "get", Route = "singleagent/status/{instanceId}")] HttpRequestData req,
+        string instanceId,
+        [DurableClient] DurableTaskClient client)
+    {
+        OrchestrationMetadata? status = await client.GetInstanceAsync(
+            instanceId,
+            getInputsAndOutputs: true,
+            req.FunctionContext.CancellationToken);
+
+        if (status is null)
+        {
+            HttpResponseData notFound = req.CreateResponse(HttpStatusCode.NotFound);
+            await notFound.WriteAsJsonAsync(new { error = "Instance not found" });
+            return notFound;
+        }
+
+        HttpResponseData response = req.CreateResponse(HttpStatusCode.OK);
+        await response.WriteAsJsonAsync(new
+        {
+            instanceId = status.InstanceId,
+            runtimeStatus = status.RuntimeStatus.ToString(),
+            input = status.SerializedInput is not null ? (object)status.ReadInputAs<JsonElement>() : null,
+            output = status.SerializedOutput is not null ? (object)status.ReadOutputAs<JsonElement>() : null,
+            failureDetails = status.FailureDetails
+        });
+        return response;
+    }
+
+    private static string GetStatusQueryGetUri(HttpRequestData req, string instanceId)
+    {
+        // NOTE: This can be made more robust by considering the value of
+        //       request headers like "X-Forwarded-Host" and "X-Forwarded-Proto".
+        string authority = $"{req.Url.Scheme}://{req.Url.Authority}";
+        return $"{authority}/api/singleagent/status/{instanceId}";
+    }
+}
@@ -0,0 +1,40 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Hosting.AzureFunctions;
+using Microsoft.Azure.Functions.Worker.Builder;
+using Microsoft.Extensions.Hosting;
+using OpenAI;
+
+// Get the Azure OpenAI endpoint and deployment name from environment variables.
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
+
+// Single agent used by the orchestration to demonstrate sequential calls on the same thread.
+const string WriterName = "WriterAgent";
+const string WriterInstructions =
+    """
+    You refine short pieces of text. When given an initial sentence you enhance it;
+    when given an improved sentence you polish it further.
+    """;
+
+AIAgent writerAgent = client.GetChatClient(deploymentName).CreateAIAgent(WriterInstructions, WriterName);
+
+using IHost app = FunctionsApplication
+    .CreateBuilder(args)
+    .ConfigureFunctionsWebApplication()
+    .ConfigureDurableAgents(options => options.AddAIAgent(writerAgent))
+    .Build();
+
+app.Run();
@@ -0,0 +1,59 @@
+# Single Agent Orchestration Sample
+
+This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a simple Azure Functions app that orchestrates sequential calls to a single AI agent using the same conversation thread for context continuity.
+
+## Key Concepts Demonstrated
+
+- Orchestrating multiple interactions with the same agent in a deterministic order
+- Using the same `AgentThread` across multiple calls to maintain conversational context
+- Durable orchestration with automatic checkpointing and resumption from failures
+- HTTP API integration for starting and monitoring orchestrations
+
+## Environment Setup
+
+See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
+
+## Running the Sample
+
+With the environment setup and function app running, you can test the sample by sending an HTTP request to start the orchestration.
+
+You can use the `demo.http` file to start the orchestration, or a command line tool like `curl` as shown below:
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl -X POST http://localhost:7071/api/singleagent/run
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Method Post -Uri http://localhost:7071/api/singleagent/run
+```
+
+The response will be a JSON object that looks something like the following, which indicates that the orchestration has started.
+
+```json
+{
+  "message": "Single-agent orchestration started.",
+  "instanceId": "86313f1d45fb42eeb50b1852626bf3ff",
+  "statusQueryGetUri": "http://localhost:7071/api/singleagent/status/86313f1d45fb42eeb50b1852626bf3ff"
+}
+```
+
+The orchestration will proceed to run the WriterAgent twice in sequence:
+
+1. First, it writes an inspirational sentence about learning
+2. Then, it refines the initial output using the same conversation thread
+
+Once the orchestration has completed, you can get the status of the orchestration by sending a GET request to the `statusQueryGetUri` URL. The response will be a JSON object that looks something like the following:
+
+```json
+{
+    "failureDetails": null,
+    "input": null,
+    "instanceId": "86313f1d45fb42eeb50b1852626bf3ff",
+    "output": "Learning serves as the key, opening doors to boundless opportunities and a brighter future.",
+    "runtimeStatus": "Completed"
+}
+```
@@ -0,0 +1,3 @@
+### Start the single-agent orchestration
+POST http://localhost:7071/api/singleagent/run
+
@@ -0,0 +1,20 @@
+{
+  "version": "2.0",
+  "logging": {
+    "logLevel": {
+      "Microsoft.Agents.AI.DurableTask": "Information",
+      "Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
+      "DurableTask": "Information",
+      "Microsoft.DurableTask": "Information"
+    }
+  },
+  "extensions": {
+    "durableTask": {
+      "hubName": "default",
+      "storageProvider": {
+        "type": "AzureManaged",
+        "connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
+      }
+    }
+  }
+}
@@ -0,0 +1,10 @@
+{
+  "IsEncrypted": false,
+  "Values": {
+    "FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
+    "AzureWebJobsStorage": "UseDevelopmentStorage=true",
+    "DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
+    "AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
+    "AZURE_OPENAI_DEPLOYMENT": "<AZURE_OPENAI_DEPLOYMENT>"
+  }
+}
@@ -0,0 +1,42 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFramework>net9.0</TargetFramework>
+    <AzureFunctionsVersion>v4</AzureFunctionsVersion>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <!-- The Functions build tools don't like namespaces that start with a number -->
+    <AssemblyName>AgentOrchestration_Concurrency</AssemblyName>
+    <RootNamespace>AgentOrchestration_Concurrency</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <FrameworkReference Include="Microsoft.AspNetCore.App" />
+  </ItemGroup>
+
+  <!-- Azure Functions packages -->
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Azure.Functions.Worker" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
+  <!--
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
+    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+</Project>
@@ -0,0 +1,116 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Net;
+using System.Text.Json;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.Azure.Functions.Worker;
+using Microsoft.Azure.Functions.Worker.Http;
+using Microsoft.DurableTask;
+using Microsoft.DurableTask.Client;
+
+namespace AgentOrchestration_Concurrency;
+
+public static class FunctionsTriggers
+{
+    public sealed record TextResponse(string Text);
+
+    [Function(nameof(RunOrchestrationAsync))]
+    public static async Task<object> RunOrchestrationAsync([OrchestrationTrigger] TaskOrchestrationContext context)
+    {
+        // Get the prompt from the orchestration input
+        string prompt = context.GetInput<string>() ?? throw new InvalidOperationException("Prompt is required");
+
+        // Get both agents
+        DurableAIAgent physicist = context.GetAgent("PhysicistAgent");
+        DurableAIAgent chemist = context.GetAgent("ChemistAgent");
+
+        // Start both agent runs concurrently
+        Task<AgentRunResponse<TextResponse>> physicistTask = physicist.RunAsync<TextResponse>(prompt);
+
+        Task<AgentRunResponse<TextResponse>> chemistTask = chemist.RunAsync<TextResponse>(prompt);
+
+        // Wait for both tasks to complete using Task.WhenAll
+        await Task.WhenAll(physicistTask, chemistTask);
+
+        // Get the results
+        TextResponse physicistResponse = (await physicistTask).Result;
+        TextResponse chemistResponse = (await chemistTask).Result;
+
+        // Return the result as a structured, anonymous type
+        return new
+        {
+            physicist = physicistResponse.Text,
+            chemist = chemistResponse.Text,
+        };
+    }
+
+    // POST /multiagent/run
+    [Function(nameof(StartOrchestrationAsync))]
+    public static async Task<HttpResponseData> StartOrchestrationAsync(
+        [HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = "multiagent/run")] HttpRequestData req,
+        [DurableClient] DurableTaskClient client)
+    {
+        // Read the prompt from the request body
+        string? prompt = await req.ReadAsStringAsync();
+        if (string.IsNullOrWhiteSpace(prompt))
+        {
+            HttpResponseData badRequestResponse = req.CreateResponse(HttpStatusCode.BadRequest);
+            await badRequestResponse.WriteAsJsonAsync(new { error = "Prompt is required" });
+            return badRequestResponse;
+        }
+
+        string instanceId = await client.ScheduleNewOrchestrationInstanceAsync(
+            orchestratorName: nameof(RunOrchestrationAsync),
+            input: prompt);
+
+        HttpResponseData response = req.CreateResponse(HttpStatusCode.Accepted);
+        await response.WriteAsJsonAsync(new
+        {
+            message = "Multi-agent concurrent orchestration started.",
+            prompt,
+            instanceId,
+            statusQueryGetUri = GetStatusQueryGetUri(req, instanceId),
+        });
+        return response;
+    }
+
+    // GET /multiagent/status/{instanceId}
+    [Function(nameof(GetOrchestrationStatusAsync))]
+    public static async Task<HttpResponseData> GetOrchestrationStatusAsync(
+        [HttpTrigger(AuthorizationLevel.Anonymous, "get", Route = "multiagent/status/{instanceId}")] HttpRequestData req,
+        string instanceId,
+        [DurableClient] DurableTaskClient client)
+    {
+        OrchestrationMetadata? status = await client.GetInstanceAsync(
+            instanceId,
+            getInputsAndOutputs: true,
+            req.FunctionContext.CancellationToken);
+
+        if (status is null)
+        {
+            HttpResponseData notFound = req.CreateResponse(HttpStatusCode.NotFound);
+            await notFound.WriteAsJsonAsync(new { error = "Instance not found" });
+            return notFound;
+        }
+
+        HttpResponseData response = req.CreateResponse(HttpStatusCode.OK);
+        await response.WriteAsJsonAsync(new
+        {
+            instanceId = status.InstanceId,
+            runtimeStatus = status.RuntimeStatus.ToString(),
+            input = status.SerializedInput is not null ? (object)status.ReadInputAs<JsonElement>() : null,
+            output = status.SerializedOutput is not null ? (object)status.ReadOutputAs<JsonElement>() : null,
+            failureDetails = status.FailureDetails
+        });
+        return response;
+    }
+
+    private static string GetStatusQueryGetUri(HttpRequestData req, string instanceId)
+    {
+        // NOTE: This can be made more robust by considering the value of
+        //       request headers like "X-Forwarded-Host" and "X-Forwarded-Proto".
+        string authority = $"{req.Url.Scheme}://{req.Url.Authority}";
+        return $"{authority}/api/multiagent/status/{instanceId}";
+    }
+}
@@ -0,0 +1,44 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Hosting.AzureFunctions;
+using Microsoft.Azure.Functions.Worker.Builder;
+using Microsoft.Extensions.Hosting;
+using OpenAI;
+
+// Get the Azure OpenAI endpoint and deployment name from environment variables.
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
+
+// Two agents used by the orchestration to demonstrate concurrent execution.
+const string PhysicistName = "PhysicistAgent";
+const string PhysicistInstructions = "You are an expert in physics. You answer questions from a physics perspective.";
+
+const string ChemistName = "ChemistAgent";
+const string ChemistInstructions = "You are an expert in chemistry. You answer questions from a chemistry perspective.";
+
+AIAgent physicistAgent = client.GetChatClient(deploymentName).CreateAIAgent(PhysicistInstructions, PhysicistName);
+AIAgent chemistAgent = client.GetChatClient(deploymentName).CreateAIAgent(ChemistInstructions, ChemistName);
+
+using IHost app = FunctionsApplication
+    .CreateBuilder(args)
+    .ConfigureFunctionsWebApplication()
+    .ConfigureDurableAgents(options =>
+    {
+        options.AddAIAgent(physicistAgent);
+        options.AddAIAgent(chemistAgent);
+    })
+    .Build();
+
+app.Run();
@@ -0,0 +1,65 @@
+# Multi-Agent Concurrent Orchestration Sample
+
+This sample demonstrates how to use the Durable Agent Framework (DAFx) to create an Azure Functions app that orchestrates concurrent execution of multiple AI agents, each with specialized expertise, to provide comprehensive answers to complex questions.
+
+## Key Concepts Demonstrated
+
+- Multi-agent orchestration with specialized AI agents (physics and chemistry)
+- Concurrent execution using the fan-out/fan-in pattern for improved performance and distributed processing
+- Response aggregation from multiple agents into a unified result
+- Durable orchestration with automatic checkpointing and resumption from failures
+
+## Environment Setup
+
+See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
+
+## Running the Sample
+
+With the environment setup and function app running, you can test the sample by sending an HTTP request with a custom prompt to the orchestration.
+
+You can use the `demo.http` file to send a message to the agents, or a command line tool like `curl` as shown below:
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl -X POST http://localhost:7071/api/multiagent/run \
+    -H "Content-Type: text/plain" \
+    -d "What is temperature?"
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/multiagent/run `
+    -ContentType text/plain `
+    -Body "What is temperature?"
+```
+
+The response will be a JSON object that looks something like the following, which indicates that the orchestration has started.
+
+```json
+{
+  "message": "Multi-agent concurrent orchestration started.",
+  "prompt": "What is temperature?",
+  "instanceId": "e7e29999b6b8424682b3539292afc9ed",
+  "statusQueryGetUri": "http://localhost:7071/api/multiagent/status/e7e29999b6b8424682b3539292afc9ed"
+}
+```
+
+The orchestration will run both the PhysicistAgent and ChemistAgent concurrently, asking them the same question. Their responses will be combined to provide a comprehensive answer covering both physical and chemical aspects.
+
+Once the orchestration has completed, you can get the status of the orchestration by sending a GET request to the `statusQueryGetUri` URL. The response will be a JSON object that looks something like the following:
+
+```json
+{
+  "failureDetails": null,
+  "input": "What is temperature?",
+  "instanceId": "e7e29999b6b8424682b3539292afc9ed",
+  "output": {
+    "physicist": "Temperature is a measure of the average kinetic energy of particles in a system. From a physics perspective, it represents the thermal energy and determines the direction of heat flow between objects.",
+    "chemist": "From a chemistry perspective, temperature is crucial for chemical reactions as it affects reaction rates through the Arrhenius equation. It influences the equilibrium position of reversible reactions and determines the physical state of substances."
+  },
+  "runtimeStatus": "Completed"
+}
+```
@@ -0,0 +1,5 @@
+### Start the multi-agent concurrent orchestration
+POST http://localhost:7071/api/multiagent/run
+Content-Type: text/plain
+
+What is temperature?
@@ -0,0 +1,20 @@
+{
+  "version": "2.0",
+  "logging": {
+    "logLevel": {
+      "Microsoft.Agents.AI.DurableTask": "Information",
+      "Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
+      "DurableTask": "Information",
+      "Microsoft.DurableTask": "Information"
+    }
+  },
+  "extensions": {
+    "durableTask": {
+      "hubName": "default",
+      "storageProvider": {
+        "type": "AzureManaged",
+        "connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
+      }
+    }
+  }
+}
@@ -0,0 +1,10 @@
+{
+  "IsEncrypted": false,
+  "Values": {
+    "FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
+    "AzureWebJobsStorage": "UseDevelopmentStorage=true",
+    "DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
+    "AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
+    "AZURE_OPENAI_DEPLOYMENT": "<AZURE_OPENAI_DEPLOYMENT>"
+  }
+}
@@ -0,0 +1,42 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFramework>net9.0</TargetFramework>
+    <AzureFunctionsVersion>v4</AzureFunctionsVersion>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <!-- The Functions build tools don't like namespaces that start with a number -->
+    <AssemblyName>AgentOrchestration_Conditionals</AssemblyName>
+    <RootNamespace>AgentOrchestration_Conditionals</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <FrameworkReference Include="Microsoft.AspNetCore.App" />
+  </ItemGroup>
+
+  <!-- Azure Functions packages -->
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Azure.Functions.Worker" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
+  <!--
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
+    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+</Project>
@@ -0,0 +1,143 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Net;
+using System.Text.Json;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.Azure.Functions.Worker;
+using Microsoft.Azure.Functions.Worker.Http;
+using Microsoft.DurableTask;
+using Microsoft.DurableTask.Client;
+
+namespace AgentOrchestration_Conditionals;
+
+public static class FunctionTriggers
+{
+    [Function(nameof(RunOrchestrationAsync))]
+    public static async Task<string> RunOrchestrationAsync([OrchestrationTrigger] TaskOrchestrationContext context)
+    {
+        // Get the email from the orchestration input
+        Email email = context.GetInput<Email>() ?? throw new InvalidOperationException("Email is required");
+
+        // Get the spam detection agent
+        DurableAIAgent spamDetectionAgent = context.GetAgent("SpamDetectionAgent");
+        AgentThread spamThread = spamDetectionAgent.GetNewThread();
+
+        // Step 1: Check if the email is spam
+        AgentRunResponse<DetectionResult> spamDetectionResponse = await spamDetectionAgent.RunAsync<DetectionResult>(
+            message:
+                $"""
+                Analyze this email for spam content and return a JSON response with 'is_spam' (boolean) and 'reason' (string) fields:
+                Email ID: {email.EmailId}
+                Content: {email.EmailContent}
+                """,
+            thread: spamThread);
+        DetectionResult result = spamDetectionResponse.Result;
+
+        // Step 2: Conditional logic based on spam detection result
+        if (result.IsSpam)
+        {
+            // Handle spam email
+            return await context.CallActivityAsync<string>(nameof(HandleSpamEmail), result.Reason);
+        }
+
+        // Generate and send response for legitimate email
+        DurableAIAgent emailAssistantAgent = context.GetAgent("EmailAssistantAgent");
+        AgentThread emailThread = emailAssistantAgent.GetNewThread();
+
+        AgentRunResponse<EmailResponse> emailAssistantResponse = await emailAssistantAgent.RunAsync<EmailResponse>(
+            message:
+                $"""
+                    Draft a professional response to this email. Return a JSON response with a 'response' field containing the reply:
+                    
+                    Email ID: {email.EmailId}
+                    Content: {email.EmailContent}
+                    """,
+            thread: emailThread);
+
+        EmailResponse emailResponse = emailAssistantResponse.Result;
+
+        return await context.CallActivityAsync<string>(nameof(SendEmail), emailResponse.Response);
+    }
+
+    [Function(nameof(HandleSpamEmail))]
+    public static string HandleSpamEmail([ActivityTrigger] string reason)
+    {
+        return $"Email marked as spam: {reason}";
+    }
+
+    [Function(nameof(SendEmail))]
+    public static string SendEmail([ActivityTrigger] string message)
+    {
+        return $"Email sent: {message}";
+    }
+
+    // POST /spamdetection/run
+    [Function(nameof(StartOrchestrationAsync))]
+    public static async Task<HttpResponseData> StartOrchestrationAsync(
+        [HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = "spamdetection/run")] HttpRequestData req,
+        [DurableClient] DurableTaskClient client)
+    {
+        // Read the email from the request body
+        Email? email = await req.ReadFromJsonAsync<Email>();
+        if (email is null || string.IsNullOrWhiteSpace(email.EmailContent))
+        {
+            HttpResponseData badRequestResponse = req.CreateResponse(HttpStatusCode.BadRequest);
+            await badRequestResponse.WriteAsJsonAsync(new { error = "Email with content is required" });
+            return badRequestResponse;
+        }
+
+        string instanceId = await client.ScheduleNewOrchestrationInstanceAsync(
+            orchestratorName: nameof(RunOrchestrationAsync),
+            input: email);
+
+        HttpResponseData response = req.CreateResponse(HttpStatusCode.Accepted);
+        await response.WriteAsJsonAsync(new
+        {
+            message = "Spam detection orchestration started.",
+            emailId = email.EmailId,
+            instanceId,
+            statusQueryGetUri = GetStatusQueryGetUri(req, instanceId),
+        });
+        return response;
+    }
+
+    // GET /spamdetection/status/{instanceId}
+    [Function(nameof(GetOrchestrationStatusAsync))]
+    public static async Task<HttpResponseData> GetOrchestrationStatusAsync(
+        [HttpTrigger(AuthorizationLevel.Anonymous, "get", Route = "spamdetection/status/{instanceId}")] HttpRequestData req,
+        string instanceId,
+        [DurableClient] DurableTaskClient client)
+    {
+        OrchestrationMetadata? status = await client.GetInstanceAsync(
+            instanceId,
+            getInputsAndOutputs: true,
+            req.FunctionContext.CancellationToken);
+
+        if (status is null)
+        {
+            HttpResponseData notFound = req.CreateResponse(HttpStatusCode.NotFound);
+            await notFound.WriteAsJsonAsync(new { error = "Instance not found" });
+            return notFound;
+        }
+
+        HttpResponseData response = req.CreateResponse(HttpStatusCode.OK);
+        await response.WriteAsJsonAsync(new
+        {
+            instanceId = status.InstanceId,
+            runtimeStatus = status.RuntimeStatus.ToString(),
+            input = status.SerializedInput is not null ? (object)status.ReadInputAs<JsonElement>() : null,
+            output = status.SerializedOutput is not null ? (object)status.ReadOutputAs<JsonElement>() : null,
+            failureDetails = status.FailureDetails
+        });
+        return response;
+    }
+
+    private static string GetStatusQueryGetUri(HttpRequestData req, string instanceId)
+    {
+        // NOTE: This can be made more robust by considering the value of
+        //       request headers like "X-Forwarded-Host" and "X-Forwarded-Proto".
+        string authority = $"{req.Url.Scheme}://{req.Url.Authority}";
+        return $"{authority}/api/spamdetection/status/{instanceId}";
+    }
+}
@@ -0,0 +1,38 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Text.Json.Serialization;
+
+namespace AgentOrchestration_Conditionals;
+
+/// <summary>
+/// Represents an email input for spam detection and response generation.
+/// </summary>
+public sealed class Email
+{
+    [JsonPropertyName("email_id")]
+    public string EmailId { get; set; } = string.Empty;
+
+    [JsonPropertyName("email_content")]
+    public string EmailContent { get; set; } = string.Empty;
+}
+
+/// <summary>
+/// Represents the result of spam detection analysis.
+/// </summary>
+public sealed class DetectionResult
+{
+    [JsonPropertyName("is_spam")]
+    public bool IsSpam { get; set; }
+
+    [JsonPropertyName("reason")]
+    public string Reason { get; set; } = string.Empty;
+}
+
+/// <summary>
+/// Represents a generated email response.
+/// </summary>
+public sealed class EmailResponse
+{
+    [JsonPropertyName("response")]
+    public string Response { get; set; } = string.Empty;
+}
@@ -0,0 +1,47 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Hosting.AzureFunctions;
+using Microsoft.Azure.Functions.Worker.Builder;
+using Microsoft.Extensions.Hosting;
+using OpenAI;
+
+// Get the Azure OpenAI endpoint and deployment name from environment variables.
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
+
+// Two agents used by the orchestration to demonstrate conditional logic.
+const string SpamDetectionName = "SpamDetectionAgent";
+const string SpamDetectionInstructions = "You are a spam detection assistant that identifies spam emails.";
+
+const string EmailAssistantName = "EmailAssistantAgent";
+const string EmailAssistantInstructions = "You are an email assistant that helps users draft responses to emails with professionalism.";
+
+AIAgent spamDetectionAgent = client.GetChatClient(deploymentName)
+    .CreateAIAgent(SpamDetectionInstructions, SpamDetectionName);
+
+AIAgent emailAssistantAgent = client.GetChatClient(deploymentName)
+    .CreateAIAgent(EmailAssistantInstructions, EmailAssistantName);
+
+using IHost app = FunctionsApplication
+    .CreateBuilder(args)
+    .ConfigureFunctionsWebApplication()
+    .ConfigureDurableAgents(options =>
+    {
+        options.AddAIAgent(spamDetectionAgent);
+        options.AddAIAgent(emailAssistantAgent);
+    })
+    .Build();
+
+app.Run();
@@ -0,0 +1,113 @@
+# Multi-Agent Orchestration with Conditionals Sample
+
+This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a multi-agent orchestration workflow that includes conditional logic. The workflow implements a spam detection system that processes emails and takes different actions based on whether the email is identified as spam or legitimate.
+
+## Key Concepts Demonstrated
+
+- Multi-agent orchestration with conditional logic and different processing paths
+- Spam detection using AI agent analysis
+- Structured output from agents for reliable processing
+- Activity functions for integrating non-agentic workflow actions
+
+## Environment Setup
+
+See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
+
+## Running the Sample
+
+With the environment setup and function app running, you can test the sample by sending an HTTP request with email data to the orchestration.
+
+You can use the `demo.http` file to send email data to the agents, or a command line tool like `curl` as shown below:
+
+Bash (Linux/macOS/WSL):
+
+```bash
+# Test with a legitimate email
+curl -X POST http://localhost:7071/api/spamdetection/run \
+    -H "Content-Type: application/json" \
+    -d '{
+      "email_id": "email-001",
+      "email_content": "Hi John, I hope you are doing well. I wanted to follow up on our meeting yesterday about the quarterly report. Could you please send me the updated figures by Friday? Thanks!"
+    }'
+
+# Test with a spam email
+curl -X POST http://localhost:7071/api/spamdetection/run \
+    -H "Content-Type: application/json" \
+    -d '{
+      "email_id": "email-002",
+      "email_content": "URGENT! You have won $1,000,000! Click here now to claim your prize! Limited time offer! Do not miss out!"
+    }'
+```
+
+PowerShell:
+
+```powershell
+# Test with a legitimate email
+$body = @{
+    email_id = "email-001"
+    email_content = "Hi John, I hope you are doing well. I wanted to follow up on our meeting yesterday about the quarterly report. Could you please send me the updated figures by Friday? Thanks!"
+} | ConvertTo-Json
+
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/spamdetection/run `
+    -ContentType application/json `
+    -Body $body
+
+# Test with a spam email
+$body = @{
+    email_id = "email-002"
+    email_content = "URGENT! You have won $1,000,000! Click here now to claim your prize! Limited time offer! Do not miss out!"
+} | ConvertTo-Json
+
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/spamdetection/run `
+    -ContentType application/json `
+    -Body $body
+```
+
+The response from either input will be a JSON object that looks something like the following, which indicates that the orchestration has started.
+
+```json
+{
+  "message": "Spam detection orchestration started.",
+  "emailId": "email-001",
+  "instanceId": "555dbbb63f75406db2edf9f1f092de95",
+  "statusQueryGetUri": "http://localhost:7071/api/spamdetection/status/555dbbb63f75406db2edf9f1f092de95"
+}
+```
+
+The orchestration will:
+
+1. Analyze the email content using the SpamDetectionAgent
+2. If spam: Mark the email as spam with a reason
+3. If legitimate: Use the EmailAssistantAgent to draft a professional response and "send" it
+
+Once the orchestration has completed, you can get the status of the orchestration by sending a GET request to the `statusQueryGetUri` URL. The response for the legitimate email will be a JSON object that looks something like the following:
+
+```json
+{
+  "failureDetails": null,
+  "input": {
+    "email_content": "Hi John, I hope you're doing well. I wanted to follow up on our meeting yesterday about the quarterly report. Could you please send me the updated figures by Friday? Thanks!",
+    "email_id": "email-001"
+  },
+  "instanceId": "555dbbb63f75406db2edf9f1f092de95",
+  "output": "Email sent: Subject: Re: Follow-Up on Quarterly Report\n\nHi [Recipient's Name],\n\nI hope this message finds you well. Thank you for your patience. I will ensure the updated figures for the quarterly report are sent to you by Friday.\n\nIf you have any further questions or need additional information, please feel free to reach out.\n\nBest regards,\n\nJohn",
+  "runtimeStatus": "Completed"
+}
+```
+
+The response for the spam email will be a JSON object that looks something like the following, which indicates that the email was marked as spam:
+
+```json
+{
+  "failureDetails": null,
+  "input": {
+    "email_content": "URGENT! You have won $1,000,000! Click here now to claim your prize! Limited time offer! Do not miss out!",
+    "email_id": "email-002"
+  },
+  "instanceId": "555dbbb63f75406db2edf9f1f092de95",
+  "output": "Email marked as spam: The email contains misleading claims of winning a large sum of money and encourages immediate action, which are common characteristics of spam.",
+  "runtimeStatus": "Completed"
+}
+```
@@ -0,0 +1,18 @@
+### Test spam detection with a legitimate email
+POST http://localhost:7071/api/spamdetection/run
+Content-Type: application/json
+
+{
+  "email_id": "email-001",
+  "email_content": "Hi John, I hope you're doing well. I wanted to follow up on our meeting yesterday about the quarterly report. Could you please send me the updated figures by Friday? Thanks!"
+}
+
+
+### Test spam detection with a spam email
+POST http://localhost:7071/api/spamdetection/run
+Content-Type: application/json
+
+{
+  "email_id": "email-002", 
+  "email_content": "URGENT! You've won $1,000,000! Click here now to claim your prize! Limited time offer! Don't miss out!"
+}
@@ -0,0 +1,20 @@
+{
+  "version": "2.0",
+  "logging": {
+    "logLevel": {
+      "Microsoft.Agents.AI.DurableTask": "Information",
+      "Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
+      "DurableTask": "Information",
+      "Microsoft.DurableTask": "Information"
+    }
+  },
+  "extensions": {
+    "durableTask": {
+      "hubName": "default",
+      "storageProvider": {
+        "type": "AzureManaged",
+        "connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
+      }
+    }
+  }
+}
@@ -0,0 +1,10 @@
+{
+  "IsEncrypted": false,
+  "Values": {
+    "FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
+    "AzureWebJobsStorage": "UseDevelopmentStorage=true",
+    "DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
+    "AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
+    "AZURE_OPENAI_DEPLOYMENT": "<AZURE_OPENAI_DEPLOYMENT>"
+  }
+}
@@ -0,0 +1,43 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFramework>net9.0</TargetFramework>
+    <AzureFunctionsVersion>v4</AzureFunctionsVersion>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <!-- The Functions build tools don't like namespaces that start with a number -->
+    <AssemblyName>AgentOrchestration_HITL</AssemblyName>
+    <RootNamespace>AgentOrchestration_HITL</RootNamespace>
+    <NoWarn>$(NoWarn);DURABLE0001;DURABLE0002;DURABLE0003;DURABLE0004;DURABLE0005;DURABLE0006</NoWarn>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <FrameworkReference Include="Microsoft.AspNetCore.App" />
+  </ItemGroup>
+
+  <!-- Azure Functions packages -->
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Azure.Functions.Worker" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
+  <!--
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
+    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+</Project>
@@ -0,0 +1,229 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Net;
+using System.Text.Json;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.Azure.Functions.Worker;
+using Microsoft.Azure.Functions.Worker.Http;
+using Microsoft.DurableTask;
+using Microsoft.DurableTask.Client;
+using Microsoft.Extensions.Logging;
+
+namespace AgentOrchestration_HITL;
+
+public static class FunctionTriggers
+{
+    [Function(nameof(RunOrchestrationAsync))]
+    public static async Task<object> RunOrchestrationAsync(
+        [OrchestrationTrigger] TaskOrchestrationContext context)
+    {
+        // Get the input from the orchestration
+        ContentGenerationInput input = context.GetInput<ContentGenerationInput>()
+            ?? throw new InvalidOperationException("Content generation input is required");
+
+        // Get the writer agent
+        DurableAIAgent writerAgent = context.GetAgent("WriterAgent");
+        AgentThread writerThread = writerAgent.GetNewThread();
+
+        // Set initial status
+        context.SetCustomStatus($"Starting content generation for topic: {input.Topic}");
+
+        // Step 1: Generate initial content
+        AgentRunResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
+            message: $"Write a short article about '{input.Topic}'.",
+            thread: writerThread);
+        GeneratedContent content = writerResponse.Result;
+
+        // Human-in-the-loop iteration - we set a maximum number of attempts to avoid infinite loops
+        int iterationCount = 0;
+        while (iterationCount++ < input.MaxReviewAttempts)
+        {
+            context.SetCustomStatus(
+                $"Requesting human feedback. Iteration #{iterationCount}. Timeout: {input.ApprovalTimeoutHours} hour(s).");
+
+            // Step 2: Notify user to review the content
+            await context.CallActivityAsync(nameof(NotifyUserForApproval), content);
+
+            // Step 3: Wait for human feedback with configurable timeout
+            HumanApprovalResponse humanResponse;
+            try
+            {
+                humanResponse = await context.WaitForExternalEvent<HumanApprovalResponse>(
+                    eventName: "HumanApproval",
+                    timeout: TimeSpan.FromHours(input.ApprovalTimeoutHours));
+            }
+            catch (OperationCanceledException)
+            {
+                // Timeout occurred - treat as rejection
+                context.SetCustomStatus(
+                    $"Human approval timed out after {input.ApprovalTimeoutHours} hour(s). Treating as rejection.");
+                throw new TimeoutException($"Human approval timed out after {input.ApprovalTimeoutHours} hour(s).");
+            }
+
+            if (humanResponse.Approved)
+            {
+                context.SetCustomStatus("Content approved by human reviewer. Publishing content...");
+
+                // Step 4: Publish the approved content
+                await context.CallActivityAsync(nameof(PublishContent), content);
+
+                context.SetCustomStatus($"Content published successfully at {context.CurrentUtcDateTime:s}");
+                return new { content = content.Content };
+            }
+
+            context.SetCustomStatus("Content rejected by human reviewer. Incorporating feedback and regenerating...");
+
+            // Incorporate human feedback and regenerate
+            writerResponse = await writerAgent.RunAsync<GeneratedContent>(
+                message: $"""
+                    The content was rejected by a human reviewer. Please rewrite the article incorporating their feedback.
+                    
+                    Human Feedback: {humanResponse.Feedback}
+                    """,
+                thread: writerThread);
+
+            content = writerResponse.Result;
+        }
+
+        // If we reach here, it means we exhausted the maximum number of iterations
+        throw new InvalidOperationException(
+            $"Content could not be approved after {input.MaxReviewAttempts} iterations.");
+    }
+
+    // POST /hitl/run
+    [Function(nameof(StartOrchestrationAsync))]
+    public static async Task<HttpResponseData> StartOrchestrationAsync(
+        [HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = "hitl/run")] HttpRequestData req,
+        [DurableClient] DurableTaskClient client)
+    {
+        // Read the input from the request body
+        ContentGenerationInput? input = await req.ReadFromJsonAsync<ContentGenerationInput>();
+        if (input is null || string.IsNullOrWhiteSpace(input.Topic))
+        {
+            HttpResponseData badRequestResponse = req.CreateResponse(HttpStatusCode.BadRequest);
+            await badRequestResponse.WriteAsJsonAsync(new { error = "Topic is required" });
+            return badRequestResponse;
+        }
+
+        string instanceId = await client.ScheduleNewOrchestrationInstanceAsync(
+            orchestratorName: nameof(RunOrchestrationAsync),
+            input: input);
+
+        HttpResponseData response = req.CreateResponse(HttpStatusCode.Accepted);
+        await response.WriteAsJsonAsync(new
+        {
+            message = "HITL content generation orchestration started.",
+            topic = input.Topic,
+            instanceId,
+            statusQueryGetUri = GetStatusQueryGetUri(req, instanceId),
+        });
+        return response;
+    }
+
+    // POST /hitl/approve/{instanceId}
+    [Function(nameof(SendHumanApprovalAsync))]
+    public static async Task<HttpResponseData> SendHumanApprovalAsync(
+        [HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = "hitl/approve/{instanceId}")] HttpRequestData req,
+        string instanceId,
+        [DurableClient] DurableTaskClient client)
+    {
+        // Read the approval response from the request body
+        HumanApprovalResponse? approvalResponse = await req.ReadFromJsonAsync<HumanApprovalResponse>();
+        if (approvalResponse is null)
+        {
+            HttpResponseData badRequestResponse = req.CreateResponse(HttpStatusCode.BadRequest);
+            await badRequestResponse.WriteAsJsonAsync(new { error = "Approval response is required" });
+            return badRequestResponse;
+        }
+
+        // Send the approval event to the orchestration
+        await client.RaiseEventAsync(instanceId, "HumanApproval", approvalResponse);
+
+        HttpResponseData response = req.CreateResponse(HttpStatusCode.OK);
+        await response.WriteAsJsonAsync(new
+        {
+            message = "Human approval sent to orchestration.",
+            instanceId,
+            approved = approvalResponse.Approved
+        });
+        return response;
+    }
+
+    // GET /hitl/status/{instanceId}
+    [Function(nameof(GetOrchestrationStatusAsync))]
+    public static async Task<HttpResponseData> GetOrchestrationStatusAsync(
+        [HttpTrigger(AuthorizationLevel.Anonymous, "get", Route = "hitl/status/{instanceId}")] HttpRequestData req,
+        string instanceId,
+        [DurableClient] DurableTaskClient client)
+    {
+        OrchestrationMetadata? status = await client.GetInstanceAsync(
+            instanceId,
+            getInputsAndOutputs: true,
+            req.FunctionContext.CancellationToken);
+
+        if (status is null)
+        {
+            HttpResponseData notFound = req.CreateResponse(HttpStatusCode.NotFound);
+            await notFound.WriteAsJsonAsync(new { error = "Instance not found" });
+            return notFound;
+        }
+
+        HttpResponseData response = req.CreateResponse(HttpStatusCode.OK);
+        await response.WriteAsJsonAsync(new
+        {
+            instanceId = status.InstanceId,
+            runtimeStatus = status.RuntimeStatus.ToString(),
+            workflowStatus = status.SerializedCustomStatus is not null ? (object)status.ReadCustomStatusAs<JsonElement>() : null,
+            input = status.SerializedInput is not null ? (object)status.ReadInputAs<JsonElement>() : null,
+            output = status.SerializedOutput is not null ? (object)status.ReadOutputAs<JsonElement>() : null,
+            failureDetails = status.FailureDetails
+        });
+        return response;
+    }
+
+    [Function(nameof(NotifyUserForApproval))]
+    public static void NotifyUserForApproval(
+        [ActivityTrigger] GeneratedContent content,
+        FunctionContext functionContext)
+    {
+        ILogger logger = functionContext.GetLogger(nameof(NotifyUserForApproval));
+
+        // In a real implementation, this would send notifications via email, SMS, etc.
+        logger.LogInformation(
+            """
+            NOTIFICATION: Please review the following content for approval:
+            Title: {Title}
+            Content: {Content}
+            Use the approval endpoint to approve or reject this content.
+            """,
+            content.Title,
+            content.Content);
+    }
+
+    [Function(nameof(PublishContent))]
+    public static void PublishContent(
+        [ActivityTrigger] GeneratedContent content,
+        FunctionContext functionContext)
+    {
+        ILogger logger = functionContext.GetLogger(nameof(PublishContent));
+
+        // In a real implementation, this would publish to a CMS, website, etc.
+        logger.LogInformation(
+            """
+            PUBLISHING: Content has been published successfully.
+            Title: {Title}
+            Content: {Content}
+            """,
+            content.Title,
+            content.Content);
+    }
+
+    private static string GetStatusQueryGetUri(HttpRequestData req, string instanceId)
+    {
+        // NOTE: This can be made more robust by considering the value of
+        //       request headers like "X-Forwarded-Host" and "X-Forwarded-Proto".
+        string authority = $"{req.Url.Scheme}://{req.Url.Authority}";
+        return $"{authority}/api/hitl/status/{instanceId}";
+    }
+}
@@ -0,0 +1,44 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Text.Json.Serialization;
+
+namespace AgentOrchestration_HITL;
+
+/// <summary>
+/// Represents the input for the Human-in-the-Loop content generation workflow.
+/// </summary>
+public sealed class ContentGenerationInput
+{
+    [JsonPropertyName("topic")]
+    public string Topic { get; set; } = string.Empty;
+
+    [JsonPropertyName("max_review_attempts")]
+    public int MaxReviewAttempts { get; set; } = 3;
+
+    [JsonPropertyName("approval_timeout_hours")]
+    public float ApprovalTimeoutHours { get; set; } = 72;
+}
+
+/// <summary>
+/// Represents the content generated by the writer agent.
+/// </summary>
+public sealed class GeneratedContent
+{
+    [JsonPropertyName("title")]
+    public string Title { get; set; } = string.Empty;
+
+    [JsonPropertyName("content")]
+    public string Content { get; set; } = string.Empty;
+}
+
+/// <summary>
+/// Represents the human approval response.
+/// </summary>
+public sealed class HumanApprovalResponse
+{
+    [JsonPropertyName("approved")]
+    public bool Approved { get; set; }
+
+    [JsonPropertyName("feedback")]
+    public string Feedback { get; set; } = string.Empty;
+}
@@ -0,0 +1,40 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Hosting.AzureFunctions;
+using Microsoft.Azure.Functions.Worker.Builder;
+using Microsoft.Extensions.Hosting;
+using OpenAI;
+
+// Get the Azure OpenAI endpoint and deployment name from environment variables.
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
+
+// Single agent used by the orchestration to demonstrate human-in-the-loop workflow.
+const string WriterName = "WriterAgent";
+const string WriterInstructions =
+    """
+    You are a professional content writer who creates high-quality articles on various topics.
+    You write engaging, informative, and well-structured content that follows best practices for readability and accuracy.
+    """;
+
+AIAgent writerAgent = client.GetChatClient(deploymentName).CreateAIAgent(WriterInstructions, WriterName);
+
+using IHost app = FunctionsApplication
+    .CreateBuilder(args)
+    .ConfigureFunctionsWebApplication()
+    .ConfigureDurableAgents(options => options.AddAIAgent(writerAgent))
+    .Build();
+
+app.Run();
@@ -0,0 +1,126 @@
+# Multi-Agent Orchestration with Human-in-the-Loop Sample
+
+This sample demonstrates how to use the Durable Agent Framework (DAFx) to create a human-in-the-loop (HITL) workflow using a single AI agent. The workflow uses a writer agent to generate content and requires human approval on every iteration, emphasizing the human-in-the-loop pattern.
+
+## Key Concepts Demonstrated
+
+- Single-agent orchestration
+- Human-in-the-loop feedback loop using external events (`WaitForExternalEvent`)
+- Activity functions for non-agentic workflow steps
+- Iterative content refinement based on human feedback
+- Custom status tracking for workflow visibility
+- Error handling with maximum retry attempts and timeout handling for human approval
+
+## Environment Setup
+
+See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
+
+## Running the Sample
+
+With the environment setup and function app running, you can test the sample by sending an HTTP request with a topic to start the content generation workflow.
+
+You can use the `demo.http` file to send a topic to the agents, or a command line tool like `curl` as shown below:
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl -X POST http://localhost:7071/api/hitl/run \
+    -H "Content-Type: application/json" \
+    -d '{
+      "topic": "The Future of Artificial Intelligence",
+      "max_review_attempts": 3,
+      "timeout_minutes": 5
+    }'
+```
+
+PowerShell:
+
+```powershell
+$body = @{
+    topic = "The Future of Artificial Intelligence"
+    max_review_attempts = 3
+    timeout_minutes = 5
+} | ConvertTo-Json
+
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/hitl/run `
+    -ContentType application/json `
+    -Body $body
+```
+
+The response will be a JSON object that looks something like the following, which indicates that the orchestration has started.
+
+```json
+{
+  "message": "HITL content generation orchestration started.",
+  "topic": "The Future of Artificial Intelligence",
+  "instanceId": "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6",
+  "statusQueryGetUri": "http://localhost:7071/api/hitl/status/a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6"
+}
+```
+
+The orchestration will:
+
+1. Generate initial content using the WriterAgent
+2. Notify the user to review the content
+3. Wait for human feedback via external event (configurable timeout)
+4. If approved by human, publish the content
+5. If rejected by human, incorporate feedback and regenerate content
+6. If approval timeout occurs, treat as rejection and fail the orchestration
+7. Repeat until human approval is received or maximum loop iterations are reached
+
+Once the orchestration is waiting for human approval, you can send approval or rejection using the approval endpoint:
+
+Bash (Linux/macOS/WSL):
+
+```bash
+# Approve the content
+curl -X POST http://localhost:7071/api/hitl/approve/a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6 \
+    -H "Content-Type: application/json" \
+    -d '{
+      "approved": true,
+      "feedback": "Great article! The content is well-structured and informative."
+    }'
+
+# Reject the content with feedback
+curl -X POST http://localhost:7071/api/hitl/approve/a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6 \
+    -H "Content-Type: application/json" \
+    -d '{
+      "approved": false,
+      "feedback": "The article needs more technical depth and better examples."
+    }'
+```
+
+PowerShell:
+
+```powershell
+# Approve the content
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/hitl/approve/a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6 `
+    -ContentType application/json `
+    -Body '{ "approved": true, "feedback": "Great article! The content is well-structured and informative." }'
+
+# Reject the content with feedback
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/hitl/approve/a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6 `
+    -ContentType application/json `
+    -Body '{ "approved": false, "feedback": "The article needs more technical depth and better examples." }'
+```
+
+Once the orchestration has completed, you can get the status by sending a GET request to the `statusQueryGetUri` URL. The response will be a JSON object that looks something like the following:
+
+```json
+{
+  "failureDetails": null,
+  "input": {
+    "topic": "The Future of Artificial Intelligence",
+    "max_review_attempts": 3
+  },
+  "instanceId": "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6",
+  "output": {
+    "content": "The Future of Artificial Intelligence is..."
+  },
+  "runtimeStatus": "Completed",
+  "workflowStatus": "Content published successfully at 2025-10-15T12:00:00Z"
+}
+```
@@ -0,0 +1,44 @@
+### Start the HITL content generation orchestration with default timeout (30 days)
+POST http://localhost:7071/api/hitl/run
+Content-Type: application/json
+
+{
+  "topic": "The Future of Artificial Intelligence",
+  "max_review_attempts": 3
+}
+
+
+### Start the HITL content generation orchestration with very short timeout for demonstration (~4 seconds)
+POST http://localhost:7071/api/hitl/run
+Content-Type: application/json
+
+{
+  "topic": "The Future of Artificial Intelligence",
+  "max_review_attempts": 3,
+  "approval_timeout_hours": 0.001
+}
+
+
+### Copy/paste the instanceId from the response above
+@instanceId=INSTANCE_ID_GOES_HERE
+
+### Check the status of the orchestration (replace {instanceId} with the actual instance ID from the response above)
+GET http://localhost:7071/api/hitl/status/{{instanceId}}
+
+### Send human approval (replace {instanceId} with the actual instance ID)
+POST http://localhost:7071/api/hitl/approve/{{instanceId}}
+Content-Type: application/json
+
+{
+  "approved": true,
+  "feedback": "Great article! The content is well-structured and informative."
+}
+
+### Send human rejection with feedback (replace {instanceId} with the actual instance ID)
+POST http://localhost:7071/api/hitl/approve/{{instanceId}}
+Content-Type: application/json
+
+{
+  "approved": false,
+  "feedback": "The article needs more technical depth and better examples. Please add more specific use cases and implementation details."
+}
@@ -0,0 +1,20 @@
+{
+  "version": "2.0",
+  "logging": {
+    "logLevel": {
+      "Microsoft.Agents.AI.DurableTask": "Information",
+      "Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
+      "DurableTask": "Information",
+      "Microsoft.DurableTask": "Information"
+    }
+  },
+  "extensions": {
+    "durableTask": {
+      "hubName": "default",
+      "storageProvider": {
+        "type": "AzureManaged",
+        "connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
+      }
+    }
+  }
+}
@@ -0,0 +1,10 @@
+{
+  "IsEncrypted": false,
+  "Values": {
+    "FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
+    "AzureWebJobsStorage": "UseDevelopmentStorage=true",
+    "DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
+    "AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
+    "AZURE_OPENAI_DEPLOYMENT": "<AZURE_OPENAI_DEPLOYMENT>"
+  }
+}
@@ -0,0 +1,42 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFramework>net9.0</TargetFramework>
+    <AzureFunctionsVersion>v4</AzureFunctionsVersion>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <!-- The Functions build tools don't like namespaces that start with a number -->
+    <AssemblyName>LongRunningTools</AssemblyName>
+    <RootNamespace>LongRunningTools</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <FrameworkReference Include="Microsoft.AspNetCore.App" />
+  </ItemGroup>
+
+  <!-- Azure Functions packages -->
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Azure.Functions.Worker" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
+  <!--
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
+    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+</Project>
@@ -0,0 +1,151 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.Azure.Functions.Worker;
+using Microsoft.DurableTask;
+using Microsoft.Extensions.Logging;
+
+namespace LongRunningTools;
+
+public static class FunctionTriggers
+{
+    [Function(nameof(RunOrchestrationAsync))]
+    public static async Task<object> RunOrchestrationAsync(
+        [OrchestrationTrigger] TaskOrchestrationContext context)
+    {
+        // Get the input from the orchestration
+        ContentGenerationInput input = context.GetInput<ContentGenerationInput>()
+            ?? throw new InvalidOperationException("Content generation input is required");
+
+        // Get the writer agent
+        DurableAIAgent writerAgent = context.GetAgent("Writer");
+        AgentThread writerThread = writerAgent.GetNewThread();
+
+        // Set initial status
+        context.SetCustomStatus($"Starting content generation for topic: {input.Topic}");
+
+        // Step 1: Generate initial content
+        AgentRunResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
+            message: $"Write a short article about '{input.Topic}'.",
+            thread: writerThread);
+        GeneratedContent content = writerResponse.Result;
+
+        // Human-in-the-loop iteration - we set a maximum number of attempts to avoid infinite loops
+        int iterationCount = 0;
+        while (iterationCount++ < input.MaxReviewAttempts)
+        {
+            context.SetCustomStatus(
+                new
+                {
+                    message = "Requesting human feedback.",
+                    approvalTimeoutHours = input.ApprovalTimeoutHours,
+                    iterationCount,
+                    content
+                });
+
+            // Step 2: Notify user to review the content
+            await context.CallActivityAsync(nameof(NotifyUserForApproval), content);
+
+            // Step 3: Wait for human feedback with configurable timeout
+            HumanApprovalResponse humanResponse;
+            try
+            {
+                humanResponse = await context.WaitForExternalEvent<HumanApprovalResponse>(
+                    eventName: "HumanApproval",
+                    timeout: TimeSpan.FromHours(input.ApprovalTimeoutHours));
+            }
+            catch (OperationCanceledException)
+            {
+                // Timeout occurred - treat as rejection
+                context.SetCustomStatus(
+                    new
+                    {
+                        message = $"Human approval timed out after {input.ApprovalTimeoutHours} hour(s). Treating as rejection.",
+                        iterationCount,
+                        content
+                    });
+                throw new TimeoutException($"Human approval timed out after {input.ApprovalTimeoutHours} hour(s).");
+            }
+
+            if (humanResponse.Approved)
+            {
+                context.SetCustomStatus(new
+                {
+                    message = "Content approved by human reviewer. Publishing content...",
+                    content
+                });
+
+                // Step 4: Publish the approved content
+                await context.CallActivityAsync(nameof(PublishContent), content);
+
+                context.SetCustomStatus(new
+                {
+                    message = $"Content published successfully at {context.CurrentUtcDateTime:s}",
+                    humanFeedback = humanResponse,
+                    content
+                });
+                return new { content = content.Content };
+            }
+
+            context.SetCustomStatus(new
+            {
+                message = "Content rejected by human reviewer. Incorporating feedback and regenerating...",
+                humanFeedback = humanResponse,
+                content
+            });
+
+            // Incorporate human feedback and regenerate
+            writerResponse = await writerAgent.RunAsync<GeneratedContent>(
+                message: $"""
+                    The content was rejected by a human reviewer. Please rewrite the article incorporating their feedback.
+                    
+                    Human Feedback: {humanResponse.Feedback}
+                    """,
+                thread: writerThread);
+
+            content = writerResponse.Result;
+        }
+
+        // If we reach here, it means we exhausted the maximum number of iterations
+        throw new InvalidOperationException(
+            $"Content could not be approved after {input.MaxReviewAttempts} iterations.");
+    }
+
+    [Function(nameof(NotifyUserForApproval))]
+    public static void NotifyUserForApproval(
+        [ActivityTrigger] GeneratedContent content,
+        FunctionContext functionContext)
+    {
+        ILogger logger = functionContext.GetLogger(nameof(NotifyUserForApproval));
+
+        // In a real implementation, this would send notifications via email, SMS, etc.
+        logger.LogInformation(
+            """
+            NOTIFICATION: Please review the following content for approval:
+            Title: {Title}
+            Content: {Content}
+            Use the approval endpoint to approve or reject this content.
+            """,
+            content.Title,
+            content.Content);
+    }
+
+    [Function(nameof(PublishContent))]
+    public static void PublishContent(
+        [ActivityTrigger] GeneratedContent content,
+        FunctionContext functionContext)
+    {
+        ILogger logger = functionContext.GetLogger(nameof(PublishContent));
+
+        // In a real implementation, this would publish to a CMS, website, etc.
+        logger.LogInformation(
+            """
+            PUBLISHING: Content has been published successfully.
+            Title: {Title}
+            Content: {Content}
+            """,
+            content.Title,
+            content.Content);
+    }
+}
@@ -0,0 +1,44 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Text.Json.Serialization;
+
+namespace LongRunningTools;
+
+/// <summary>
+/// Represents the input for the content generation workflow.
+/// </summary>
+public sealed class ContentGenerationInput
+{
+    [JsonPropertyName("topic")]
+    public string Topic { get; set; } = string.Empty;
+
+    [JsonPropertyName("max_review_attempts")]
+    public int MaxReviewAttempts { get; set; } = 3;
+
+    [JsonPropertyName("approval_timeout_hours")]
+    public float ApprovalTimeoutHours { get; set; } = 72;
+}
+
+/// <summary>
+/// Represents the content generated by the writer agent.
+/// </summary>
+public sealed class GeneratedContent
+{
+    [JsonPropertyName("title")]
+    public string Title { get; set; } = string.Empty;
+
+    [JsonPropertyName("content")]
+    public string Content { get; set; } = string.Empty;
+}
+
+/// <summary>
+/// Represents the human approval response.
+/// </summary>
+public sealed class HumanApprovalResponse
+{
+    [JsonPropertyName("approved")]
+    public bool Approved { get; set; }
+
+    [JsonPropertyName("feedback")]
+    public string Feedback { get; set; } = string.Empty;
+}
@@ -0,0 +1,73 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using LongRunningTools;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Hosting.AzureFunctions;
+using Microsoft.Azure.Functions.Worker.Builder;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+using Microsoft.Extensions.Logging;
+using OpenAI;
+
+// Get the Azure OpenAI endpoint and deployment name from environment variables.
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
+
+// Agent used by the orchestration to write content.
+const string WriterAgentName = "Writer";
+const string WriterAgentInstructions =
+    """
+    You are a professional content writer who creates high-quality articles on various topics.
+    You write engaging, informative, and well-structured content that follows best practices for readability and accuracy.
+    """;
+
+AIAgent writerAgent = client.GetChatClient(deploymentName).CreateAIAgent(WriterAgentInstructions, WriterAgentName);
+
+// Agent that can start content generation workflows using tools
+const string PublisherAgentName = "Publisher";
+const string PublisherAgentInstructions =
+    """
+    You are a publishing agent that can manage content generation workflows.
+    You have access to tools to start, monitor, and raise events for content generation workflows.
+    """;
+
+using IHost app = FunctionsApplication
+    .CreateBuilder(args)
+    .ConfigureFunctionsWebApplication()
+    .ConfigureDurableAgents(options =>
+    {
+        // Add the writer agent used by the orchestration
+        options.AddAIAgent(writerAgent);
+
+        // Define the agent that can start orchestrations from tool calls
+        options.AddAIAgentFactory(PublisherAgentName, sp =>
+        {
+            // Initialize the tools to be used by the agent.
+            Tools publisherTools = new(sp.GetRequiredService<ILogger<Tools>>());
+
+            return client.GetChatClient(deploymentName).CreateAIAgent(
+                instructions: PublisherAgentInstructions,
+                name: PublisherAgentName,
+                services: sp,
+                tools: [
+                    AIFunctionFactory.Create(publisherTools.StartContentGenerationWorkflow),
+                    AIFunctionFactory.Create(publisherTools.GetWorkflowStatusAsync),
+                    AIFunctionFactory.Create(publisherTools.SubmitHumanApprovalAsync),
+                ]);
+        });
+    })
+    .Build();
+
+app.Run();
@@ -0,0 +1,129 @@
+# Long Running Tools Sample
+
+This sample demonstrates how to use the Durable Agent Framework (DAFx) to create agents with long running tools. This sample builds on the [05_AgentOrchestration_HITL](../05_AgentOrchestration_HITL) sample by adding a publisher agent that can start and manage content generation workflows. A key difference is that the publisher agent knows the IDs of the workflows it starts, so it can check the status of the workflows and approve or reject them without being explicitly given the context (instance IDs, etc).
+
+## Key Concepts Demonstrated
+
+The same key concepts as the [05_AgentOrchestration_HITL](../05_AgentOrchestration_HITL) sample are demonstrated, but with the following additional concepts:
+
+- **Long running tools**: Using `DurableAgentContext.Current` to start orchestrations from tool calls
+- **Multi-agent orchestration**: Agents can start and manage workflows that orchestrate other agents
+- **Human-in-the-loop (with delegation)**: The agent acts as an intermediary between the human and the workflow. The human remains in the loop, but delegates to the agent to start the workflow and approve or reject the content.
+
+## Environment Setup
+
+See the [README.md](../README.md) file in the parent directory for more information on how to configure the environment, including how to install and run common sample dependencies.
+
+## Running the Sample
+
+With the environment setup and function app running, you can test the sample by sending an HTTP request to start the agent, which will then trigger the content generation workflow.
+
+You can use the `demo.http` file to send requests to the agent, or a command line tool like `curl` as shown below.
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl -i -X POST http://localhost:7071/api/agents/publisher/run \
+    -D headers.txt \
+    -H "Content-Type: text/plain" \
+    -d 'Start a content generation workflow for the topic \"The Future of Artificial Intelligence\"'
+
+# Save the thread ID to a variable and print it to the terminal
+threadId=$(cat headers.txt | grep "x-ms-thread-id" | cut -d' ' -f2)
+echo "Thread ID: $threadId"
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/agents/publisher/run `
+    -ResponseHeadersVariable ResponseHeaders `
+    -ContentType text/plain `
+    -Body 'Start a content generation workflow for the topic \"The Future of Artificial Intelligence\"' `
+
+# Save the thread ID to a variable and print it to the console
+$threadId = $ResponseHeaders['x-ms-thread-id']
+Write-Host "Thread ID: $threadId"
+```
+
+The response will be a text string that looks something like the following, indicating that the agent request has been received and will be processed:
+
+```http
+HTTP/1.1 200 OK
+Content-Type: text/plain
+x-ms-thread-id: @publisher@351ec855-7f4d-4527-a60d-498301ced36d
+
+The content generation workflow for the topic "The Future of Artificial Intelligence" has been successfully started, and the instance ID is **6a04276e8d824d8d941e1dc4142cc254**. If you need any further assistance or updates on the workflow, feel free to ask!
+```
+
+The `x-ms-thread-id` response header contains the thread ID, which can be used to continue the conversation by passing it as a query parameter (`thread_id`) to the `run` endpoint. The commands above show how to save the thread ID to a `$threadId` variable for use in subsequent requests.
+
+Behind the scenes, the publisher agent will:
+
+1. Start the content generation workflow via a tool call
+1. The workflow will generate initial content using the Writer agent and wait for human approval, which will be visible in the logs
+
+Once the workflow is waiting for human approval, you can send approval or rejection by prompting the publisher agent accordingly (e.g. "Approve the content" or "Reject the content with feedback: The article needs more technical depth and better examples."):
+
+Bash (Linux/macOS/WSL):
+
+```bash
+# Approve the content
+curl -X POST "http://localhost:7071/api/agents/publisher/run?thread_id=$threadId" \
+    -H "Content-Type: text/plain" \
+    -d 'Approve the content'
+
+# Reject the content with feedback
+curl -X POST "http://localhost:7071/api/agents/publisher/run?thread_id=$threadId" \
+    -H "Content-Type: text/plain" \
+    -d 'Reject the content with feedback: The article needs more technical depth and better examples.'
+```
+
+PowerShell:
+
+```powershell
+# Approve the content
+Invoke-RestMethod -Method Post `
+    -Uri "http://localhost:7071/api/agents/publisher/run?thread_id=$threadId" `
+    -ContentType text/plain `
+    -Body 'Approve the content'
+
+# Reject the content with feedback
+Invoke-RestMethod -Method Post `
+    -Uri "http://localhost:7071/api/agents/publisher/run?thread_id=$threadId" `
+    -ContentType text/plain `
+    -Body 'Reject the content with feedback: The article needs more technical depth and better examples.'
+```
+
+Once the workflow has completed, you can get the status by prompting the publisher agent to give you the status.
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl -X POST "http://localhost:7071/api/agents/publisher/run?thread_id=$threadId" \
+    -H "Content-Type: text/plain" \
+    -d 'Get the status of the workflow you previously started'
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Method Post `
+    -Uri "http://localhost:7071/api/agents/publisher/run?thread_id=$threadId" `
+    -ContentType text/plain `
+    -Body 'Get the status of the workflow you previously started'
+```
+
+The response from the publisher agent will look something like the following:
+
+```text
+The status of the workflow with instance ID **ab1076d6e7ec49d8a2c2474d09b69ded** is as follows:
+
+- **Execution Status:** Completed
+- **Workflow Status:** Content published successfully at `2025-10-24T20:42:02`
+- **Created At:** `2025-10-24T20:41:40.7531781+00:00`
+- **Last Updated At:** `2025-10-24T20:42:02.1410736+00:00`
+
+The content has been successfully published.
+```
@@ -0,0 +1,84 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.ComponentModel;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.DurableTask.Client;
+using Microsoft.Extensions.Logging;
+
+namespace LongRunningTools;
+
+/// <summary>
+/// Tools that demonstrate starting orchestrations from agent tool calls.
+/// </summary>
+internal sealed class Tools(ILogger<Tools> logger)
+{
+    private readonly ILogger<Tools> _logger = logger;
+
+    [Description("Starts a content generation workflow and returns the instance ID for tracking.")]
+    public string StartContentGenerationWorkflow([Description("The topic for content generation")] string topic)
+    {
+        this._logger.LogInformation("Starting content generation workflow for topic: {Topic}", topic);
+
+        const int MaxReviewAttempts = 3;
+        const float ApprovalTimeoutHours = 72;
+
+        // Schedule the orchestration, which will start running after the tool call completes.
+        string instanceId = DurableAgentContext.Current.ScheduleNewOrchestration(
+            name: nameof(FunctionTriggers.RunOrchestrationAsync),
+            input: new ContentGenerationInput
+            {
+                Topic = topic,
+                MaxReviewAttempts = MaxReviewAttempts,
+                ApprovalTimeoutHours = ApprovalTimeoutHours
+            });
+
+        this._logger.LogInformation(
+            "Content generation workflow scheduled to be started for topic '{Topic}' with instance ID: {InstanceId}",
+            topic,
+            instanceId);
+
+        return $"Workflow started with instance ID: {instanceId}";
+    }
+
+    [Description("Gets the status of a workflow orchestration.")]
+    public async Task<object> GetWorkflowStatusAsync(
+        [Description("The instance ID of the workflow to check")] string instanceId,
+        [Description("Whether to include detailed information")] bool includeDetails = true)
+    {
+        this._logger.LogInformation("Getting status for workflow instance: {InstanceId}", instanceId);
+
+        // Get the current agent context using the thread-static property
+        OrchestrationMetadata? status = await DurableAgentContext.Current.GetOrchestrationStatusAsync(
+            instanceId,
+            includeDetails);
+
+        if (status is null)
+        {
+            this._logger.LogInformation("Workflow instance '{InstanceId}' not found.", instanceId);
+            return new
+            {
+                instanceId,
+                error = $"Workflow instance '{instanceId}' not found.",
+            };
+        }
+
+        return new
+        {
+            instanceId = status.InstanceId,
+            createdAt = status.CreatedAt,
+            executionStatus = status.RuntimeStatus,
+            workflowStatus = status.SerializedCustomStatus,
+            lastUpdatedAt = status.LastUpdatedAt,
+            failureDetails = status.FailureDetails
+        };
+    }
+
+    [Description("Raises a feedback event for the content generation workflow.")]
+    public async Task SubmitHumanApprovalAsync(
+        [Description("The instance ID of the workflow to submit feedback for")] string instanceId,
+        [Description("Feedback to submit")] HumanApprovalResponse feedback)
+    {
+        this._logger.LogInformation("Submitting human approval for workflow instance: {InstanceId}", instanceId);
+        await DurableAgentContext.Current.RaiseOrchestrationEventAsync(instanceId, "HumanApproval", feedback);
+    }
+}
@@ -0,0 +1,27 @@
+### Run an agent that can schedule orchestrations as tool calls
+POST http://localhost:7071/api/agents/publisher/run
+Content-Type: text/plain
+
+Start a content generation workflow for the topic 'The Future of Artificial Intelligence'
+
+
+### Save the session ID from the response to continue the conversation
+@threadId = <YOUR_THREAD_ID>
+
+### Check the status of the workflow
+POST http://localhost:7071/api/agents/publisher/run?thread_id={{threadId}}
+Content-Type: text/plain
+
+Check the status of the workflow you previously started
+
+### Reject content with feedback
+POST http://localhost:7071/api/agents/publisher/run?thread_id={{threadId}}
+Content-Type: text/plain
+
+Reject the content with feedback: The article needs more technical depth and better examples.
+
+### Approve content
+POST http://localhost:7071/api/agents/publisher/run?thread_id={{threadId}}
+Content-Type: text/plain
+
+Approve the content
@@ -0,0 +1,20 @@
+{
+  "version": "2.0",
+  "logging": {
+    "logLevel": {
+      "Microsoft.Agents.AI.DurableTask": "Information",
+      "Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
+      "DurableTask": "Information",
+      "Microsoft.DurableTask": "Information"
+    }
+  },
+  "extensions": {
+    "durableTask": {
+      "hubName": "default",
+      "storageProvider": {
+        "type": "AzureManaged",
+        "connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
+      }
+    }
+  }
+}
@@ -0,0 +1,10 @@
+{
+  "IsEncrypted": false,
+  "Values": {
+    "FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
+    "AzureWebJobsStorage": "UseDevelopmentStorage=true",
+    "DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
+    "AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
+    "AZURE_OPENAI_DEPLOYMENT": "<AZURE_OPENAI_DEPLOYMENT>"
+  }
+}
@@ -0,0 +1,42 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFramework>net9.0</TargetFramework>
+    <AzureFunctionsVersion>v4</AzureFunctionsVersion>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <!-- The Functions build tools don't like namespaces that start with a number -->
+    <AssemblyName>AgentAsMcpTool</AssemblyName>
+    <RootNamespace>AgentAsMcpTool</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <FrameworkReference Include="Microsoft.AspNetCore.App" />
+  </ItemGroup>
+
+  <!-- Azure Functions packages -->
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Azure.Functions.Worker" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.DurableTask.AzureManaged" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Extensions.Http.AspNetCore" />
+    <PackageReference Include="Microsoft.Azure.Functions.Worker.Sdk" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <!-- Local projects that should be switched to package references when using the sample outside of this MAF repo -->
+  <!--
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Agents.AI.Hosting.AzureFunctions" />
+    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+</Project>
@@ -0,0 +1,53 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to configure AI agents to be accessible as MCP tools.
+// When using AddAIAgent and enabling MCP tool triggers, the Functions host will automatically
+// generate a remote MCP endpoint for the app at /runtime/webhooks/mcp with a agent-specific
+// query tool name.
+
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.Agents.AI.Hosting.AzureFunctions;
+using Microsoft.Azure.Functions.Worker.Builder;
+using Microsoft.Extensions.Hosting;
+using OpenAI;
+
+// Get the Azure OpenAI endpoint and deployment name from environment variables.
+string endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT is not set.");
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
+
+// Define three AI agents we are going to use in this application.
+AIAgent agent1 = client.GetChatClient(deploymentName).CreateAIAgent("You are good at telling jokes.", "Joker");
+
+AIAgent agent2 = client.GetChatClient(deploymentName)
+    .CreateAIAgent("Check stock prices.", "StockAdvisor");
+
+AIAgent agent3 = client.GetChatClient(deploymentName)
+    .CreateAIAgent("Recommend plants.", "PlantAdvisor", description: "Get plant recommendations.");
+
+using IHost app = FunctionsApplication
+    .CreateBuilder(args)
+    .ConfigureFunctionsWebApplication()
+    .ConfigureDurableAgents(options =>
+    {
+        options
+        .AddAIAgent(agent1)  // Enables HTTP trigger by default.
+        .AddAIAgent(agent2, enableHttpTrigger: false, enableMcpToolTrigger: true) // Disable HTTP trigger, enable MCP Tool trigger.
+        .AddAIAgent(agent3, agentOptions =>
+        {
+            agentOptions.McpToolTrigger.IsEnabled = true; // Enable MCP Tool trigger.
+        });
+    })
+    .Build();
+app.Run();
@@ -0,0 +1,87 @@
+# Agent as MCP Tool Sample
+
+This sample demonstrates how to configure AI agents to be accessible as both HTTP endpoints and [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) tools, enabling flexible integration patterns for AI agent consumption.
+
+## Key Concepts Demonstrated
+
+- **Multi-trigger Agent Configuration**: Configure agents to support HTTP triggers, MCP tool triggers, or both
+- **Microsoft Agent Framework Integration**: Use the framework to define AI agents with specific roles and capabilities
+- **Flexible Agent Registration**: Register agents with customizable trigger configurations
+- **MCP Server Hosting**: Expose agents as MCP tools for consumption by MCP-compatible clients
+
+## Sample Architecture
+
+This sample creates three agents with different trigger configurations:
+
+| Agent | Role | HTTP Trigger | MCP Tool Trigger | Description |
+|-------|------|--------------|------------------|-------------|
+| **Joker** | Comedy specialist | ✅ Enabled | ❌ Disabled | Accessible only via HTTP requests |
+| **StockAdvisor** | Financial data | ❌ Disabled | ✅ Enabled | Accessible only as MCP tool |
+| **PlantAdvisor** | Indoor plant recommendations | ✅ Enabled | ✅ Enabled | Accessible via both HTTP and MCP |
+
+## Environment Setup
+
+See the [README.md](../README.md) file in the parent directory for complete setup instructions, including:
+
+- Prerequisites installation
+- Azure OpenAI configuration
+- Durable Task Scheduler setup
+- Storage emulator configuration
+
+For this sample, you'll also need to install [node.js](https://nodejs.org/en/download) in order to use the [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector) tool.
+
+## Configuration
+
+Update your `local.settings.json` with your Azure OpenAI credentials:
+
+```json
+{
+  "Values": {
+    "AZURE_OPENAI_ENDPOINT": "https://your-resource.openai.azure.com/",
+    "AZURE_OPENAI_DEPLOYMENT": "your-deployment-name",
+    "AZURE_OPENAI_KEY": "your-api-key-if-not-using-rbac"
+  }
+}
+```
+
+## Running the Sample
+
+1. **Start the Function App**:
+
+   ```bash
+   cd dotnet/samples/AzureFunctions/07_AgentAsMcpTool
+   func start
+   ```
+
+2. **Note the MCP Server Endpoint**: When the app starts, you'll see the MCP server endpoint in the terminal output. It will look like:
+
+   ```text
+   MCP server endpoint:  http://localhost:7071/runtime/webhooks/mcp
+   ```
+
+## Testing MCP Tool Integration
+
+Any MCP-compatible client can connect to the server endpoint and utilize the exposed agent tools. The agents will appear as callable tools within the MCP protocol.
+
+### Using MCP Inspector
+
+1. Run the [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector) from the command line:
+
+   ```bash
+   npx @modelcontextprotocol/inspector
+   ```
+
+1. Connect using the MCP server endpoint from your terminal output
+
+   - For **Transport Type**, select **"Streamable HTTP"**
+   - For **URL**, enter the MCP server endpoint `http://localhost:7071/runtime/webhooks/mcp`
+   - Click the **Connect** button
+
+1. Click the **List Tools** button to see the available MCP tools. You should see the `StockAdvisor` and `PlantAdvisor` tools.
+
+1. Test the available MCP tools:
+
+   - **StockAdvisor** - Set "MSFT ATH" (ATH is "all time high") as the query and click the **Run Tool** button.
+   - **PlantAdvisor** - Set "Low light in Seattle" as the query and click the **Run Tool** button.
+
+You'll see the results of the tool calls in the MCP Inspector interface under the **Tool Results** section. You should also see the results in the terminal where you ran the `func start` command.
@@ -0,0 +1,19 @@
+{
+  "version": "2.0",
+  "logging": {
+    "logLevel": {
+      "Microsoft.Azure.Functions.DurableAgents": "Information",
+      "DurableTask": "Information",
+      "Microsoft.DurableTask": "Information"
+    }
+  },
+  "extensions": {
+    "durableTask": {
+      "hubName": "default",
+      "storageProvider": {
+        "type": "AzureManaged",
+        "connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
+      }
+    }
+  }
+}
@@ -0,0 +1,10 @@
+{
+  "IsEncrypted": false,
+  "Values": {
+    "FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
+    "AzureWebJobsStorage": "UseDevelopmentStorage=true",
+    "DURABLE_TASK_SCHEDULER_CONNECTION_STRING": "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None",
+    "AZURE_OPENAI_ENDPOINT": "<AZURE_OPENAI_ENDPOINT>",
+    "AZURE_OPENAI_DEPLOYMENT": "<AZURE_OPENAI_DEPLOYMENT>"
+  }
+}
@@ -0,0 +1,151 @@
+# Azure Functions Samples
+
+This directory contains samples for Azure Functions.
+
+- **[01_SingleAgent](01_SingleAgent)**: A sample that demonstrates how to host a single conversational agent in an Azure Functions app and invoke it directly over HTTP.
+- **[02_AgentOrchestration_Chaining](02_AgentOrchestration_Chaining)**: A sample that demonstrates how to host a single conversational agent in an Azure Functions app and invoke it using a durable orchestration.
+- **[03_AgentOrchestration_Concurrency](03_AgentOrchestration_Concurrency)**: A sample that demonstrates how to host multiple agents in an Azure Functions app and run them concurrently using a durable orchestration.
+- **[04_AgentOrchestration_Conditionals](04_AgentOrchestration_Conditionals)**: A sample that demonstrates how to host multiple agents in an Azure Functions app and run them sequentially using a durable orchestration with conditionals.
+- **[05_AgentOrchestration_HITL](05_AgentOrchestration_HITL)**: A sample that demonstrates how to implement a human-in-the-loop workflow using durable orchestration, including external event handling for human approval.
+- **[06_LongRunningTools](06_LongRunningTools)**: A sample that demonstrates how agents can start and interact with durable orchestrations from tool calls to enable long-running tool scenarios.
+- **[07_AgentAsMcpTool](07_AgentAsMcpTool)**: A sample that demonstrates how to configure durable AI agents to be accessible as Model Context Protocol (MCP) tools.
+
+## Running the Samples
+
+These samples are designed to be run locally in a cloned repository.
+
+### Prerequisites
+
+The following prerequisites are required to run the samples:
+
+- [.NET 9.0 SDK or later](https://dotnet.microsoft.com/download/dotnet)
+- [Azure Functions Core Tools](https://learn.microsoft.com/azure/azure-functions/functions-run-local) (version 4.x or later)
+- [Azure CLI](https://learn.microsoft.com/cli/azure/install-azure-cli) installed and authenticated (`az login`) or an API key for the Azure OpenAI service
+- [Azure OpenAI Service](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource) with a deployed model (gpt-4o-mini or better is recommended)
+- [Durable Task Scheduler](https://learn.microsoft.com/azure/azure-functions/durable/durable-task-scheduler/develop-with-durable-task-scheduler) (local emulator or Azure-hosted)
+- [Docker](https://docs.docker.com/get-docker/) installed if running the Durable Task Scheduler emulator locally
+
+### Configuring RBAC Permissions for Azure OpenAI
+
+These samples are configured to use the Azure OpenAI service with RBAC permissions to access the model. You'll need to configure the RBAC permissions for the Azure OpenAI service to allow the Azure Functions app to access the model.
+
+Below is an example of how to configure the RBAC permissions for the Azure OpenAI service to allow the current user to access the model.
+
+Bash (Linux/macOS/WSL):
+
+```bash
+az role assignment create \
+    --assignee "yourname@contoso.com" \
+    --role "Cognitive Services OpenAI User" \
+    --scope /subscriptions/<your-subscription-id>/resourceGroups/<your-resource-group-name>/providers/Microsoft.CognitiveServices/accounts/<your-openai-resource-name>
+```
+
+PowerShell:
+
+```powershell
+az role assignment create `
+    --assignee "yourname@contoso.com" `
+    --role "Cognitive Services OpenAI User" `
+    --scope /subscriptions/<your-subscription-id>/resourceGroups/<your-resource-group-name>/providers/Microsoft.CognitiveServices/accounts/<your-openai-resource-name>
+```
+
+More information on how to configure RBAC permissions for Azure OpenAI can be found in the [Azure OpenAI documentation](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=cli).
+
+### Setting an API key for the Azure OpenAI service
+
+As an alternative to configuring Azure RBAC permissions, you can set an API key for the Azure OpenAI service by setting the `AZURE_OPENAI_KEY` environment variable.
+
+Bash (Linux/macOS/WSL):
+
+```bash
+export AZURE_OPENAI_KEY="your-api-key"
+```
+
+PowerShell:
+
+```powershell
+$env:AZURE_OPENAI_KEY="your-api-key"
+```
+
+### Start Durable Task Scheduler
+
+Most samples use the Durable Task Scheduler (DTS) to support hosted agents and durable orchestrations. DTS also allows you to view the status of orchestrations and their inputs and outputs from a web UI.
+
+To run the Durable Task Scheduler locally, you can use the following `docker` command:
+
+```bash
+docker run -d --name dts-emulator -p 8080:8080 -p 8082:8082 mcr.microsoft.com/dts/dts-emulator:latest
+```
+
+The DTS dashboard will be available at `http://localhost:8080`.
+
+### Start the Azure Storage Emulator
+
+All Function apps require an Azure Storage account to store functions-specific state. You can use the Azure Storage Emulator to run a local instance of the Azure Storage service.
+
+You can run the Azure Storage emulator locally as a standalone process or via a Docker container.
+
+#### Docker
+
+```bash
+docker run -d --name storage-emulator -p 10000:10000 -p 10001:10001 -p 10002:10002 mcr.microsoft.com/azure-storage/azurite
+```
+
+#### Standalone
+
+```bash
+npm install -g azurite
+azurite
+```
+
+### Environment Configuration
+
+Each sample has its own `local.settings.json` file that contains the environment variables for the sample. You'll need to update the `local.settings.json` file with the correct values for your Azure OpenAI resource.
+
+```json
+{
+  "Values": {
+    "AZURE_OPENAI_ENDPOINT": "https://your-resource.openai.azure.com/",
+    "AZURE_OPENAI_DEPLOYMENT": "your-deployment-name"
+  }
+}
+```
+
+Alternatively, you can set the environment variables in the command line.
+
+### Bash (Linux/macOS/WSL)
+
+```bash
+export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
+export AZURE_OPENAI_DEPLOYMENT="your-deployment-name"
+```
+
+### PowerShell
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
+$env:AZURE_OPENAI_DEPLOYMENT="your-deployment-name"
+```
+
+These environment variables, when set, will override the values in the `local.settings.json` file, making it convenient to test the sample without having to update the `local.settings.json` file.
+
+### Start the Azure Functions app
+
+Navigate to the sample directory and start the Azure Functions app:
+
+```bash
+cd dotnet/samples/AzureFunctions/01_SingleAgent
+func start
+```
+
+The Azure Functions app will be available at `http://localhost:7071`.
+
+### Test the Azure Functions app
+
+The README.md file in each sample directory contains instructions for testing the sample. Each sample also includes a `demo.http` file that can be used to test the sample from the command line. These files can be opened in VS Code with the [REST Client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client) extension or in the Visual Studio IDE.
+
+### Viewing the sample output
+
+The Azure Functions app logs are displayed in the terminal where you ran `func start`. This is where most agent output will be displayed. You can adjust logging levels in the `host.json` file as needed.
+
+You can also see the state of agents and orchestrations in the DTS dashboard.
@@ -0,0 +1,21 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <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 +1,51 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create and use a AI agents with Azure Foundry Agents as the backend.
+
+using Azure.AI.Projects;
+using Azure.AI.Projects.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_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.
+var aiProjectClient = new AIProjectClient(new Uri(endpoint), new AzureCliCredential());
+
+// Define the agent you want to create. (Prompt Agent in this case)
+var agentVersionCreationOptions = new AgentVersionCreationOptions(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.
+var agentVersion = aiProjectClient.Agents.CreateAgentVersion(agentName: JokerName, options: agentVersionCreationOptions);
+
+// Note:
+//      agentVersion.Id = "<agentName>:<versionNumber>",
+//      agentVersion.Version = <versionNumber>,
+//      agentVersion.Name = <agentName>
+
+// You can retrieve an AIAgent for a already created server side agent version.
+AIAgent jokerAgentV1 = aiProjectClient.GetAIAgent(agentVersion);
+
+// You can also create another AIAgent version (V2) by providing the same name with a different definition.
+AIAgent jokerAgentV2 = aiProjectClient.CreateAIAgent(name: JokerName, model: deploymentName, instructions: JokerInstructions + "V2");
+
+// You can also get the AIAgent latest version just providing its name.
+AIAgent jokerAgentLatest = aiProjectClient.GetAIAgent(name: JokerName);
+var latestVersion = jokerAgentLatest.GetService<AgentVersion>()!;
+
+// The AIAgent version can be accessed via the GetService method.
+Console.WriteLine($"Latest agent version id: {latestVersion.Id}");
+
+// Once you have the AIAgent, you can invoke it like any other AIAgent.
+AgentThread thread = jokerAgentLatest.GetNewThread();
+Console.WriteLine(await jokerAgentLatest.RunAsync("Tell me a joke about a pirate.", thread));
+
+// This will use the same thread to continue the conversation.
+Console.WriteLine(await jokerAgentLatest.RunAsync("Now tell me a joke about a cat and a dog using last joke as the anchor.", thread));
+
+// Cleanup by agent name removes both agent versions created (jokerAgentV1 + jokerAgentV2).
+aiProjectClient.Agents.DeleteAgent(jokerAgentV1.Name);
@@ -0,0 +1,16 @@
+# Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 8.0 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
+$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+```
@@ -15,7 +15,8 @@ See the README.md for each sample for the prerequisites for that sample.
 |Sample|Description|
 |---|---|
 |[Creating an AIAgent with A2A](./Agent_With_A2A/)|This sample demonstrates how to create AIAgent for an existing A2A agent.|
-|[Creating an AIAgent with AzureFoundry Agent](./Agent_With_AzureFoundryAgent/)|This sample demonstrates how to create an Azure Foundry agent and expose it as an AIAgent|
+|[Creating an AIAgent with Foundry Agents using Azure.AI.Agents.Persistent](./Agent_With_AzureAIAgentsPersistent/)|This sample demonstrates how to create a Foundry Persistent agent and expose it as an AIAgent using the Azure.AI.Agents.Persistent SDK|
+|[Creating an AIAgent with Foundry Agents using Azure.AI.Project](./Agent_With_AzureAIProject/)|This sample demonstrates how to create an Foundry Project agent and expose it as an AIAgent using the Azure.AI.Project SDK|
 |[Creating an AIAgent with AzureFoundry Model](./Agent_With_AzureFoundryModel/)|This sample demonstrates how to use any model deployed to Azure Foundry to create an AIAgent|
 |[Creating an AIAgent with Azure OpenAI ChatCompletion](./Agent_With_AzureOpenAIChatCompletion/)|This sample demonstrates how to create an AIAgent using Azure OpenAI ChatCompletion as the underlying inference service|
 |[Creating an AIAgent with Azure OpenAI Responses](./Agent_With_AzureOpenAIResponses/)|This sample demonstrates how to create an AIAgent using Azure OpenAI Responses as the underlying inference service|
@@ -0,0 +1,21 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <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 +1,53 @@
+// 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.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_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.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
+
+// 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);
+
+// Note:
+//      agentVersion.Id = "<agentName>:<versionNumber>",
+//      agentVersion.Version = <versionNumber>,
+//      agentVersion.Name = <agentName>
+
+// You can retrieve an AIAgent for an already created server side agent version.
+AIAgent jokerAgentV1 = aiProjectClient.GetAIAgent(agentVersion);
+
+// You can also create another AIAgent version (V2) by providing the same name with a different definition.
+AIAgent jokerAgentV2 = aiProjectClient.CreateAIAgent(name: JokerName, model: deploymentName, instructions: JokerInstructions + "V2");
+
+// You can also get the AIAgent latest version by just providing its name.
+AIAgent jokerAgentLatest = aiProjectClient.GetAIAgent(name: JokerName);
+AgentVersion latestVersion = jokerAgentLatest.GetService<AgentVersion>()!;
+
+// The AIAgent version can be accessed via the GetService method.
+Console.WriteLine($"Latest agent version id: {latestVersion.Id}");
+
+// Once you have the AIAgent, you can invoke it like any other AIAgent.
+AgentThread thread = jokerAgentLatest.GetNewThread();
+Console.WriteLine(await jokerAgentLatest.RunAsync("Tell me a joke about a pirate.", thread));
+
+// This will use the same thread to continue the conversation.
+Console.WriteLine(await jokerAgentLatest.RunAsync("Now tell me a joke about a cat and a dog using last joke as the anchor.", thread));
+
+// Cleanup by agent name removes both agent versions created (jokerAgentV1 + jokerAgentV2).
+await aiProjectClient.Agents.DeleteAgentAsync(jokerAgentV1.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 8.0 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
+$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_FOUNDRY_PROJECT_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/GettingStarted/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
@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <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>
@@ -0,0 +1,41 @@
+// 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.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_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.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
+
+// 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 retrieve an AIAgent for a already created server side agent version.
+AIAgent jokerAgent = aiProjectClient.GetAIAgent(agentVersion);
+
+// Invoke the agent and output the text result.
+AgentThread thread = jokerAgent.GetNewThread();
+Console.WriteLine(await jokerAgent.RunAsync("Tell me a joke about a pirate.", thread));
+
+// Invoke the agent with streaming support.
+thread = jokerAgent.GetNewThread();
+await foreach (AgentRunResponseUpdate update in jokerAgent.RunStreamingAsync("Tell me a joke about a pirate.", thread))
+{
+    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 8.0 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
+$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_FOUNDRY_PROJECT_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/GettingStarted/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
+
@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <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>
@@ -0,0 +1,45 @@
+// 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.Projects;
+using Azure.AI.Projects.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_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.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
+
+// Define the agent you want to create. (Prompt Agent in this case)
+AgentVersionCreationOptions options = new(new PromptAgentDefinition(model: deploymentName) { Instructions = JokerInstructions });
+
+// Create a server side agent version with the Azure.AI.Agents SDK client.
+AgentVersion agentVersion = aiProjectClient.Agents.CreateAgentVersion(agentName: JokerName, options);
+
+// Retrieve an AIAgent for the created server side agent version.
+AIAgent jokerAgent = aiProjectClient.GetAIAgent(agentVersion);
+
+// Invoke the agent with a multi-turn conversation, where the context is preserved in the thread object.
+AgentThread thread = jokerAgent.GetNewThread();
+Console.WriteLine(await jokerAgent.RunAsync("Tell me a joke about a pirate.", thread));
+Console.WriteLine(await jokerAgent.RunAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", thread));
+
+// Invoke the agent with a multi-turn conversation and streaming, where the context is preserved in the thread object.
+thread = jokerAgent.GetNewThread();
+await foreach (AgentRunResponseUpdate update in jokerAgent.RunStreamingAsync("Tell me a joke about a pirate.", thread))
+{
+    Console.WriteLine(update);
+}
+await foreach (AgentRunResponseUpdate update in jokerAgent.RunStreamingAsync("Now add some emojis to the joke and tell it in the voice of a pirate's parrot.", thread))
+{
+    Console.WriteLine(update);
+}
+
+// Cleanup by agent name removes the agent version created.
+await aiProjectClient.Agents.DeleteAgentAsync(jokerAgent.Name);
@@ -0,0 +1,50 @@
+# 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.
+
+## What this sample demonstrates
+
+- Creating an AI agent with instructions
+- Using threads to maintain conversation context
+- Running multi-turn conversations with text output
+- Running multi-turn conversations with streaming output
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 8.0 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
+$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_FOUNDRY_PROJECT_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/GettingStarted/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 thread for conversation context
+3. Run the agent with a text prompt and display the response
+4. Send a follow-up message to the same thread, demonstrating context preservation
+5. Create a new thread and run the agent with streaming
+6. Send a follow-up streaming message to demonstrate multi-turn streaming
+7. Clean up resources by deleting the agent
+
@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <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>
@@ -0,0 +1,43 @@
+// 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_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_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.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
+
+// Define the agent with function tools.
+AITool tool = AIFunctionFactory.Create(GetWeather);
+
+// Create AIAgent directly
+AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: AssistantName, model: deploymentName, instructions: AssistantInstructions, tools: [tool]);
+
+// Non-streaming agent interaction with function tools.
+AgentThread thread = agent.GetNewThread();
+Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?", thread));
+
+// Streaming agent interaction with function tools.
+thread = agent.GetNewThread();
+await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync("What is the weather like in Amsterdam?", thread))
+{
+    Console.WriteLine(update);
+}
+
+// Cleanup by agent name removes the agent version created.
+await aiProjectClient.Agents.DeleteAgentAsync(agent.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 8.0 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_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_FOUNDRY_PROJECT_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/GettingStarted/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
+
@@ -0,0 +1,27 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.Projects" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.SemanticKernel.Plugins.OpenApi" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <None Update="OpenAPISpec.json">
+      <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
+    </None>
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,354 @@
+{
+    "openapi": "3.0.1",
+    "info": {
+        "title": "Github Versions API",
+        "version": "1.0.0"
+    },
+    "servers": [
+        {
+            "url": "https://api.github.com"
+        }
+    ],
+    "components": {
+        "schemas": {
+            "basic-error": {
+                "title": "Basic Error",
+                "description": "Basic Error",
+                "type": "object",
+                "properties": {
+                    "message": {
+                        "type": "string"
+                    },
+                    "documentation_url": {
+                        "type": "string"
+                    },
+                    "url": {
+                        "type": "string"
+                    },
+                    "status": {
+                        "type": "string"
+                    }
+                }
+            },
+            "label": {
+                "title": "Label",
+                "description": "Color-coded labels help you categorize and filter your issues (just like labels in Gmail).",
+                "type": "object",
+                "properties": {
+                    "id": {
+                        "description": "Unique identifier for the label.",
+                        "type": "integer",
+                        "format": "int64",
+                        "example": 208045946
+                    },
+                    "node_id": {
+                        "type": "string",
+                        "example": "MDU6TGFiZWwyMDgwNDU5NDY="
+                    },
+                    "url": {
+                        "description": "URL for the label",
+                        "example": "https://api.github.com/repositories/42/labels/bug",
+                        "type": "string",
+                        "format": "uri"
+                    },
+                    "name": {
+                        "description": "The name of the label.",
+                        "example": "bug",
+                        "type": "string"
+                    },
+                    "description": {
+                        "description": "Optional description of the label, such as its purpose.",
+                        "type": "string",
+                        "example": "Something isn't working",
+                        "nullable": true
+                    },
+                    "color": {
+                        "description": "6-character hex code, without the leading #, identifying the color",
+                        "example": "FFFFFF",
+                        "type": "string"
+                    },
+                    "default": {
+                        "description": "Whether this label comes by default in a new repository.",
+                        "type": "boolean",
+                        "example": true
+                    }
+                },
+                "required": [
+                    "id",
+                    "node_id",
+                    "url",
+                    "name",
+                    "description",
+                    "color",
+                    "default"
+                ]
+            },
+            "tag": {
+                "title": "Tag",
+                "description": "Tag",
+                "type": "object",
+                "properties": {
+                    "name": {
+                        "type": "string",
+                        "example": "v0.1"
+                    },
+                    "commit": {
+                        "type": "object",
+                        "properties": {
+                            "sha": {
+                                "type": "string"
+                            },
+                            "url": {
+                                "type": "string",
+                                "format": "uri"
+                            }
+                        },
+                        "required": [
+                            "sha",
+                            "url"
+                        ]
+                    },
+                    "zipball_url": {
+                        "type": "string",
+                        "format": "uri",
+                        "example": "https://github.com/octocat/Hello-World/zipball/v0.1"
+                    },
+                    "tarball_url": {
+                        "type": "string",
+                        "format": "uri",
+                        "example": "https://github.com/octocat/Hello-World/tarball/v0.1"
+                    },
+                    "node_id": {
+                        "type": "string"
+                    }
+                },
+                "required": [
+                    "name",
+                    "node_id",
+                    "commit",
+                    "zipball_url",
+                    "tarball_url"
+                ]
+            }
+        },
+        "examples": {
+            "label-items": {
+                "value": [
+                    {
+                        "id": 208045946,
+                        "node_id": "MDU6TGFiZWwyMDgwNDU5NDY=",
+                        "url": "https://api.github.com/repos/octocat/Hello-World/labels/bug",
+                        "name": "bug",
+                        "description": "Something isn't working",
+                        "color": "f29513",
+                        "default": true
+                    },
+                    {
+                        "id": 208045947,
+                        "node_id": "MDU6TGFiZWwyMDgwNDU5NDc=",
+                        "url": "https://api.github.com/repos/octocat/Hello-World/labels/enhancement",
+                        "name": "enhancement",
+                        "description": "New feature or request",
+                        "color": "a2eeef",
+                        "default": false
+                    }
+                ]
+            },
+            "tag-items": {
+                "value": [
+                    {
+                        "name": "v0.1",
+                        "commit": {
+                            "sha": "c5b97d5ae6c19d5c5df71a34c7fbeeda2479ccbc",
+                            "url": "https://api.github.com/repos/octocat/Hello-World/commits/c5b97d5ae6c19d5c5df71a34c7fbeeda2479ccbc"
+                        },
+                        "zipball_url": "https://github.com/octocat/Hello-World/zipball/v0.1",
+                        "tarball_url": "https://github.com/octocat/Hello-World/tarball/v0.1",
+                        "node_id": "MDQ6VXNlcjE="
+                    }
+                ]
+            }
+        },
+        "parameters": {
+            "owner": {
+                "name": "owner",
+                "description": "The account owner of the repository. The name is not case sensitive.",
+                "in": "path",
+                "required": true,
+                "schema": {
+                    "type": "string"
+                }
+            },
+            "repo": {
+                "name": "repo",
+                "description": "The name of the repository without the `.git` extension. The name is not case sensitive.",
+                "in": "path",
+                "required": true,
+                "schema": {
+                    "type": "string"
+                }
+            },
+            "per-page": {
+                "name": "per_page",
+                "description": "The number of results per page (max 100). For more information, see \"[Using pagination in the REST API](https://docs.github.com/rest/using-the-rest-api/using-pagination-in-the-rest-api).\"",
+                "in": "query",
+                "schema": {
+                    "type": "integer",
+                    "default": 30
+                }
+            },
+            "page": {
+                "name": "page",
+                "description": "The page number of the results to fetch. For more information, see \"[Using pagination in the REST API](https://docs.github.com/rest/using-the-rest-api/using-pagination-in-the-rest-api).\"",
+                "in": "query",
+                "schema": {
+                    "type": "integer",
+                    "default": 1
+                }
+            }
+        },
+        "responses": {
+            "not_found": {
+                "description": "Resource not found",
+                "content": {
+                    "application/json": {
+                        "schema": {
+                            "$ref": "#/components/schemas/basic-error"
+                        }
+                    }
+                }
+            }
+        },
+        "headers": {
+            "link": {
+                "example": "<https://api.github.com/resource?page=2>; rel=\"next\", <https://api.github.com/resource?page=5>; rel=\"last\"",
+                "schema": {
+                    "type": "string"
+                }
+            }
+        }
+    },
+    "paths": {
+        "/repos/{owner}/{repo}/tags": {
+            "get": {
+                "summary": "List repository tags",
+                "description": "",
+                "tags": [
+                    "repos"
+                ],
+                "operationId": "repos/list-tags",
+                "externalDocs": {
+                    "description": "API method documentation",
+                    "url": "https://docs.github.com/rest/repos/repos#list-repository-tags"
+                },
+                "parameters": [
+                    {
+                        "$ref": "#/components/parameters/owner"
+                    },
+                    {
+                        "$ref": "#/components/parameters/repo"
+                    },
+                    {
+                        "$ref": "#/components/parameters/per-page"
+                    },
+                    {
+                        "$ref": "#/components/parameters/page"
+                    }
+                ],
+                "responses": {
+                    "200": {
+                        "description": "Response",
+                        "content": {
+                            "application/json": {
+                                "schema": {
+                                    "type": "array",
+                                    "items": {
+                                        "$ref": "#/components/schemas/tag"
+                                    }
+                                },
+                                "examples": {
+                                    "default": {
+                                        "$ref": "#/components/examples/tag-items"
+                                    }
+                                }
+                            }
+                        },
+                        "headers": {
+                            "Link": {
+                                "$ref": "#/components/headers/link"
+                            }
+                        }
+                    }
+                },
+                "x-github": {
+                    "githubCloudOnly": false,
+                    "enabledForGitHubApps": true,
+                    "category": "repos",
+                    "subcategory": "repos"
+                }
+            }
+        },
+        "/repos/{owner}/{repo}/labels": {
+            "get": {
+                "summary": "List labels for a repository",
+                "description": "Lists all labels for a repository.",
+                "tags": [
+                    "issues"
+                ],
+                "operationId": "issues/list-labels-for-repo",
+                "externalDocs": {
+                    "description": "API method documentation",
+                    "url": "https://docs.github.com/rest/issues/labels#list-labels-for-a-repository"
+                },
+                "parameters": [
+                    {
+                        "$ref": "#/components/parameters/owner"
+                    },
+                    {
+                        "$ref": "#/components/parameters/repo"
+                    },
+                    {
+                        "$ref": "#/components/parameters/per-page"
+                    },
+                    {
+                        "$ref": "#/components/parameters/page"
+                    }
+                ],
+                "responses": {
+                    "200": {
+                        "description": "Response",
+                        "content": {
+                            "application/json": {
+                                "schema": {
+                                    "type": "array",
+                                    "items": {
+                                        "$ref": "#/components/schemas/label"
+                                    }
+                                },
+                                "examples": {
+                                    "default": {
+                                        "$ref": "#/components/examples/label-items"
+                                    }
+                                }
+                            }
+                        },
+                        "headers": {
+                            "Link": {
+                                "$ref": "#/components/headers/link"
+                            }
+                        }
+                    },
+                    "404": {
+                        "$ref": "#/components/responses/not_found"
+                    }
+                },
+                "x-github": {
+                    "githubCloudOnly": false,
+                    "enabledForGitHubApps": true,
+                    "category": "issues",
+                    "subcategory": "labels"
+                }
+            }
+        }
+    }
+}
@@ -0,0 +1,38 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use an agent with function tools provided via an OpenAPI spec.
+// It uses functionality from Semantic Kernel to parse the OpenAPI spec and create function tools to use with the Agent Framework Agent.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.Plugins.OpenApi;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// Load the OpenAPI Spec from a file.
+KernelPlugin plugin = await OpenApiKernelPluginFactory.CreateFromOpenApiAsync("github", "OpenAPISpec.json");
+
+// Convert the Semantic Kernel plugin to Agent Framework function tools.
+// This requires a dummy Kernel instance, since KernelFunctions cannot execute without one.
+Kernel kernel = new();
+List<AITool> tools = plugin.Select(x => x.WithKernel(kernel)).Cast<AITool>().ToList();
+
+const string AssistantInstructions = "You are a helpful assistant that can query GitHub repositories.";
+const string AssistantName = "GitHubAssistant";
+
+// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
+
+// Create AIAgent directly
+AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: AssistantName, model: deploymentName, instructions: AssistantInstructions, tools: tools);
+
+// Run the agent with the OpenAPI function tools.
+AgentThread thread = agent.GetNewThread();
+Console.WriteLine(await agent.RunAsync("Please list the names, colors and descriptions of all the labels available in the microsoft/agent-framework repository on github.", thread));
+
+// Cleanup by agent name removes the agent version created.
+await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
@@ -0,0 +1,49 @@
+# Using Function Tools from OpenAPI Specifications
+
+This sample demonstrates how to create function tools from an OpenAPI specification and use them with AI agents.
+
+## What this sample demonstrates
+
+- Loading OpenAPI specifications from files
+- Converting OpenAPI specifications to Semantic Kernel plugins
+- Converting Semantic Kernel plugins to AI function tools
+- Using OpenAPI-based function tools with AI agents
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 8.0 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_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_FOUNDRY_PROJECT_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/GettingStarted/FoundryAgents
+dotnet run --project .\FoundryAgents_Step03.2_UsingFunctionTools_FromOpenAPI
+```
+
+## Expected behavior
+
+The sample will:
+
+1. Load the OpenAPI specification from OpenAPISpec.json (GitHub API)
+2. Convert the OpenAPI spec to Semantic Kernel plugins
+3. Create an agent named "GitHubAssistant" with the OpenAPI-based function tools
+4. Run the agent with a prompt to query GitHub repositories
+5. The agent will invoke the appropriate OpenAPI function tools to retrieve data
+6. Clean up resources by deleting the agent
+
@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <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>
@@ -0,0 +1,64 @@
+// 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;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// Create a sample function tool that the agent can use.
+[Description("Get the weather for a given location.")]
+static string GetWeather([Description("The location to get the weather for.")] string location)
+    => $"The weather in {location} is cloudy with a high of 15°C.";
+
+const string AssistantInstructions = "You are a helpful assistant that can get weather information.";
+const string AssistantName = "WeatherAssistant";
+
+// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
+
+ApprovalRequiredAIFunction approvalTool = new(AIFunctionFactory.Create(GetWeather));
+
+// Create AIAgent directly
+AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: AssistantName, model: deploymentName, instructions: AssistantInstructions, tools: [approvalTool]);
+
+// Call the agent with approval-required function tools.
+// The agent will request approval before invoking the function.
+AgentThread thread = agent.GetNewThread();
+AgentRunResponse response = await agent.RunAsync("What is the weather like in Amsterdam?", thread);
+
+// Check if there are any user input requests (approvals needed).
+List<UserInputRequestContent> userInputRequests = response.UserInputRequests.ToList();
+
+while (userInputRequests.Count > 0)
+{
+    // Ask the user to approve each function call request.
+    // For simplicity, we are assuming here that only function approval requests are being made.
+    List<ChatMessage> userInputMessages = userInputRequests
+        .OfType<FunctionApprovalRequestContent>()
+        .Select(functionApprovalRequest =>
+        {
+            Console.WriteLine($"The agent would like to invoke the following function, please reply Y to approve: Name {functionApprovalRequest.FunctionCall.Name}");
+            bool approved = Console.ReadLine()?.Equals("Y", StringComparison.OrdinalIgnoreCase) ?? false;
+            return new ChatMessage(ChatRole.User, [functionApprovalRequest.CreateResponse(approved)]);
+        })
+        .ToList();
+
+    // Pass the user input responses back to the agent for further processing.
+    response = await agent.RunAsync(userInputMessages, thread);
+
+    userInputRequests = response.UserInputRequests.ToList();
+}
+
+Console.WriteLine($"\nAgent: {response}");
+
+// Cleanup by agent name removes the agent version created.
+await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
@@ -0,0 +1,51 @@
+# Using Function Tools with Approvals (Human-in-the-Loop)
+
+This sample demonstrates how to use function tools that require human approval before execution, implementing a human-in-the-loop workflow.
+
+## What this sample demonstrates
+
+- Creating approval-required function tools using ApprovalRequiredAIFunction
+- Handling user input requests for function approvals
+- Implementing human-in-the-loop approval workflows
+- Processing agent responses with pending approvals
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 8.0 SDK or later
+- Azure Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_FOUNDRY_PROJECT_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/GettingStarted/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>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <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>
@@ -0,0 +1,87 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to configure an agent to produce structured output.
+
+using System.ComponentModel;
+using System.Text.Json;
+using System.Text.Json.Serialization;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using SampleApp;
+
+#pragma warning disable CA5399
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_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.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
+
+// Create ChatClientAgent directly
+ChatClientAgent agent = await aiProjectClient.CreateAIAgentAsync(
+    model: deploymentName,
+    new ChatClientAgentOptions(name: AssistantName, instructions: AssistantInstructions)
+    {
+        ChatOptions = new()
+        {
+            ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>()
+        }
+    });
+
+// Set PersonInfo as the type parameter of RunAsync method to specify the expected structured output from the agent and invoke the agent with some unstructured input.
+AgentRunResponse<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.
+Console.WriteLine("Assistant Output:");
+Console.WriteLine($"Name: {response.Result.Name}");
+Console.WriteLine($"Age: {response.Result.Age}");
+Console.WriteLine($"Occupation: {response.Result.Occupation}");
+
+// Create the ChatClientAgent with the specified name, instructions, and expected structured output the agent should produce.
+ChatClientAgent agentWithPersonInfo = aiProjectClient.CreateAIAgent(
+    model: deploymentName,
+    new ChatClientAgentOptions(name: AssistantName, instructions: AssistantInstructions)
+    {
+        ChatOptions = new()
+        {
+            ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>()
+        }
+    });
+
+// Invoke the agent with some unstructured input while streaming, to extract the structured information from.
+IAsyncEnumerable<AgentRunResponseUpdate> 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 = (await updates.ToAgentRunResponseAsync()).Deserialize<PersonInfo>(JsonSerializerOptions.Web);
+
+Console.WriteLine("Assistant Output:");
+Console.WriteLine($"Name: {personInfo.Name}");
+Console.WriteLine($"Age: {personInfo.Age}");
+Console.WriteLine($"Occupation: {personInfo.Occupation}");
+
+// Cleanup by agent name removes the agent version created.
+await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
+
+namespace SampleApp
+{
+    /// <summary>
+    /// Represents information about a person, including their name, age, and occupation, matched to the JSON schema used in the agent.
+    /// </summary>
+    [Description("Information about a person including their name, age, and occupation")]
+    public class PersonInfo
+    {
+        [JsonPropertyName("name")]
+        public string? Name { get; set; }
+
+        [JsonPropertyName("age")]
+        public int? Age { get; set; }
+
+        [JsonPropertyName("occupation")]
+        public string? Occupation { get; set; }
+    }
+}
@@ -0,0 +1,49 @@
+# Structured Output with AI Agents
+
+This sample demonstrates how to configure AI agents to produce structured output in JSON format using JSON schemas.
+
+## What this sample demonstrates
+
+- Configuring agents with JSON schema response formats
+- Using generic RunAsync<T> method for structured output
+- Deserializing structured responses into typed objects
+- Running agents with streaming and structured output
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 8.0 SDK or later
+- Azure Foundry service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_FOUNDRY_PROJECT_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/GettingStarted/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>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <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>
@@ -0,0 +1,44 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create and use a simple AI agent with a conversation that can be persisted to disk.
+
+using System.Text.Json;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_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.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
+
+AIAgent agent = await aiProjectClient.CreateAIAgentAsync(name: JokerName, model: deploymentName, instructions: JokerInstructions);
+
+// Start a new thread for the agent conversation.
+AgentThread thread = agent.GetNewThread();
+
+// Run the agent with a new thread.
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", thread));
+
+// Serialize the thread state to a JsonElement, so it can be stored for later use.
+JsonElement serializedThread = thread.Serialize();
+
+// Save the serialized thread to a temporary file (for demonstration purposes).
+string tempFilePath = Path.GetTempFileName();
+await File.WriteAllTextAsync(tempFilePath, JsonSerializer.Serialize(serializedThread));
+
+// Load the serialized thread from the temporary file (for demonstration purposes).
+JsonElement reloadedSerializedThread = JsonSerializer.Deserialize<JsonElement>(await File.ReadAllTextAsync(tempFilePath))!;
+
+// Deserialize the thread state after loading from storage.
+AgentThread resumedThread = agent.DeserializeThread(reloadedSerializedThread);
+
+// Run the agent again with the resumed thread.
+Console.WriteLine(await agent.RunAsync("Now tell the same joke in the voice of a pirate, and add some emojis to the joke.", resumedThread));
+
+// 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 8.0 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_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_FOUNDRY_PROJECT_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/GettingStarted/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
+
@@ -0,0 +1,23 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </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" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,52 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create and use a simple AI agent with Azure Foundry Agents as the backend that logs telemetry using OpenTelemetry.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Azure.Monitor.OpenTelemetry.Exporter;
+using Microsoft.Agents.AI;
+using OpenTelemetry;
+using OpenTelemetry.Trace;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+string? applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");
+
+const string JokerInstructions = "You are good at telling jokes.";
+const string JokerName = "JokerAgent";
+
+// Create TracerProvider with console exporter
+// This will output the telemetry data to the console.
+string sourceName = Guid.NewGuid().ToString("N");
+TracerProviderBuilder tracerProviderBuilder = Sdk.CreateTracerProviderBuilder()
+    .AddSource(sourceName)
+    .AddConsoleExporter();
+if (!string.IsNullOrWhiteSpace(applicationInsightsConnectionString))
+{
+    tracerProviderBuilder.AddAzureMonitorTraceExporter(options => options.ConnectionString = applicationInsightsConnectionString);
+}
+using var tracerProvider = tracerProviderBuilder.Build();
+
+// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
+
+// Define the agent you want to create. (Prompt Agent in this case)
+AIAgent agent = aiProjectClient.CreateAIAgent(name: JokerName, model: deploymentName, instructions: JokerInstructions)
+    .AsBuilder()
+    .UseOpenTelemetry(sourceName: sourceName)
+    .Build();
+
+// Invoke the agent and output the text result.
+AgentThread thread = agent.GetNewThread();
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", thread));
+
+// Invoke the agent with streaming support.
+thread = agent.GetNewThread();
+await foreach (AgentRunResponseUpdate update in agent.RunStreamingAsync("Tell me a joke about a pirate.", thread))
+{
+    Console.WriteLine(update);
+}
+
+// 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 8.0 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_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini"  # Optional, defaults to gpt-4o-mini
+$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/GettingStarted/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
+
@@ -0,0 +1,23 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net9.0</TargetFramework>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+
+    <NoWarn>$(NoWarn);CA1812</NoWarn>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.Projects" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.Hosting" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,82 @@
+// 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 Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+
+string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+const string JokerInstructions = "You are good at telling jokes.";
+const string JokerName = "JokerAgent";
+
+// 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) => new AIProjectClient(new Uri(endpoint), new AzureCliCredential()));
+
+// Add the AI agent to the service collection.
+builder.Services.AddSingleton<AIAgent>((sp)
+    => sp.GetRequiredService<AIProjectClient>()
+        .CreateAIAgent(name: JokerName, model: deploymentName, instructions: JokerInstructions));
+
+// 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 AgentThread? _thread;
+
+    public async Task StartAsync(CancellationToken cancellationToken)
+    {
+        // Create a thread that will be used for the entirety of the service lifetime so that the user can ask follow up questions.
+        this._thread = agent.GetNewThread();
+        _ = 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 (AgentRunResponseUpdate update in agent.RunStreamingAsync(input, this._thread, 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);
+    }
+}
@@ -0,0 +1,51 @@
+# Dependency Injection with AI Agents
+
+This sample demonstrates how to use dependency injection to register and manage AI agents within a hosted service application.
+
+## What this sample demonstrates
+
+- Setting up dependency injection with HostApplicationBuilder
+- Registering AIProjectClient as a singleton service
+- Registering AIAgent as a singleton service
+- Using agents in hosted services
+- Interactive chat loop with streaming responses
+- Managing agent lifecycle (creation and deletion)
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 8.0 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_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
+$env:AZURE_FOUNDRY_PROJECT_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/GettingStarted/FoundryAgents
+dotnet run --project .\FoundryAgents_Step08_DependencyInjection
+```
+
+## Expected behavior
+
+The sample will:
+
+1. Create a host with dependency injection configured
+2. Register AIProjectClient and AIAgent as services
+3. Create an agent named "JokerAgent" with instructions to tell jokes
+4. Start an interactive chat loop where you can ask the agent questions
+5. The agent will respond with streaming output
+6. Enter an empty line or press Ctrl+C to exit
+7. Clean up resources by deleting the agent
+
--- a/Show More
+++ b/Show More