chore: import upstream snapshot with attribution

2026-07-13 13:39:25 +08:00
commit db620d33df
5151 changed files with 925932 additions and 0 deletions
@@ -0,0 +1 @@
+**/Properties/launchSettings.json
@@ -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>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <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 +1,42 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+#pragma warning disable IDE0002 // Simplify Member Access
+
+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.Chat;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// 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).AsAIAgent(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, timeToLive: TimeSpan.FromHours(1)))
+    .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=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": "ee6e47a0-f24b-40b1-ade8-16fcebb9eb40",
+  "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 +1,42 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <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");
+        AgentSession writerSession = await writer.CreateSessionAsync();
+
+        AgentResponse<TextResponse> initial = await writer.RunAsync<TextResponse>(
+            message: "Write a concise inspirational sentence about learning.",
+            session: writerSession);
+
+        AgentResponse<TextResponse> refined = await writer.RunAsync<TextResponse>(
+            message: $"Improve this further while keeping it under 25 words: {initial.Result.Text}",
+            session: writerSession);
+
+        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 +1,45 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+#pragma warning disable IDE0002 // Simplify Member Access
+
+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.Chat;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// Single agent used by the orchestration to demonstrate sequential calls on the same session.
+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).AsAIAgent(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 session for context continuity.
+
+## Key Concepts Demonstrated
+
+- Orchestrating multiple interactions with the same agent in a deterministic order
+- Using the same `AgentSession` 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 +1,42 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <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<AgentResponse<TextResponse>> physicistTask = physicist.RunAsync<TextResponse>(prompt);
+
+        Task<AgentResponse<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 +1,50 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+#pragma warning disable IDE0002 // Simplify Member Access
+
+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.Chat;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// 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).AsAIAgent(PhysicistInstructions, PhysicistName);
+AIAgent chemistAgent = client.GetChatClient(deploymentName).AsAIAgent(ChemistInstructions, ChemistName);
+
+using IHost app = FunctionsApplication
+    .CreateBuilder(args)
+    .ConfigureFunctionsWebApplication()
+    .ConfigureDurableAgents(options =>
+    {
+        options
+            .AddAIAgent(physicistAgent)
+            .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 +1,42 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <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");
+        AgentSession spamSession = await spamDetectionAgent.CreateSessionAsync();
+
+        // Step 1: Check if the email is spam
+        AgentResponse<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}
+                """,
+            session: spamSession);
+        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");
+        AgentSession emailSession = await emailAssistantAgent.CreateSessionAsync();
+
+        AgentResponse<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}
+                    """,
+            session: emailSession);
+
+        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 +1,53 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+#pragma warning disable IDE0002 // Simplify Member Access
+
+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.Chat;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// 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)
+    .AsAIAgent(SpamDetectionInstructions, SpamDetectionName);
+
+AIAgent emailAssistantAgent = client.GetChatClient(deploymentName)
+    .AsAIAgent(EmailAssistantInstructions, EmailAssistantName);
+
+using IHost app = FunctionsApplication
+    .CreateBuilder(args)
+    .ConfigureFunctionsWebApplication()
+    .ConfigureDurableAgents(options =>
+    {
+        options
+            .AddAIAgent(spamDetectionAgent)
+            .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 $1,000,000! Click here now to claim your prize! Limited time offer! Don't miss out!"
+}
@@ -0,0 +1,20 @@
+{
+  "version": "2.0",
+  "logging": {
+    "logLevel": {
+      "Microsoft.Agents.AI.DurableTask": "Information",
+      "Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
+      "DurableTask": "Information",
+      "Microsoft.DurableTask": "Information"
+    }
+  },
+  "extensions": {
+    "durableTask": {
+      "hubName": "default",
+      "storageProvider": {
+        "type": "AzureManaged",
+        "connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
+      }
+    }
+  }
+}
@@ -0,0 +1,43 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <AzureFunctionsVersion>v4</AzureFunctionsVersion>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <!-- The Functions build tools don't like namespaces that start with a number -->
+    <AssemblyName>AgentOrchestration_HITL</AssemblyName>
+    <RootNamespace>AgentOrchestration_HITL</RootNamespace>
+    <NoWarn>$(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");
+        AgentSession writerSession = await writerAgent.CreateSessionAsync();
+
+        // Set initial status
+        context.SetCustomStatus($"Starting content generation for topic: {input.Topic}");
+
+        // Step 1: Generate initial content
+        AgentResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
+            message: $"Write a short article about '{input.Topic}'.",
+            session: writerSession);
+        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}
+                    """,
+                session: writerSession);
+
+            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 +1,45 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+#pragma warning disable IDE0002 // Simplify Member Access
+
+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.Chat;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// 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).AsAIAgent(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 +1,42 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <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 +1,152 @@
+// 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");
+        AgentSession writerSession = await writerAgent.CreateSessionAsync();
+
+        // Set initial status
+        context.SetCustomStatus($"Starting content generation for topic: {input.Topic}");
+
+        // Step 1: Generate initial content
+        AgentResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
+            message: $"Write a short article about '{input.Topic}'.",
+            session: writerSession);
+        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)
+        {
+            // NOTE: CustomStatus has a 16 KB UTF-16 limit in Durable Functions.
+            // Only include short metadata here - the full content is passed via activity inputs/outputs.
+            context.SetCustomStatus(
+                new
+                {
+                    message = "Requesting human feedback.",
+                    approvalTimeoutHours = input.ApprovalTimeoutHours,
+                    iterationCount,
+                    contentTitle = content.Title,
+                });
+
+            // 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 = $"Human approval timed out after {input.ApprovalTimeoutHours} hour(s). Treating as rejection.",
+                        iterationCount,
+                    });
+                throw new TimeoutException($"Human approval timed out after {input.ApprovalTimeoutHours} hour(s).");
+            }
+
+            if (humanResponse.Approved)
+            {
+                context.SetCustomStatus(new
+                {
+                    message = "Content approved by human reviewer. Publishing content...",
+                    contentTitle = content.Title,
+                });
+
+                // 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,
+                    contentTitle = content.Title,
+                });
+                return new { content = content.Content };
+            }
+
+            context.SetCustomStatus(new
+            {
+                message = "Content rejected by human reviewer. Incorporating feedback and regenerating...",
+                humanFeedback = humanResponse,
+                contentTitle = content.Title,
+            });
+
+            // 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}
+                    """,
+                session: writerSession);
+
+            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 +1,78 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+#pragma warning disable IDE0002 // Simplify Member Access
+
+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.Chat;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// 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).AsAIAgent(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).AsAIAgent(
+                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: $threadId"
+```
+
+The response will be a text string that looks something like the following, indicating that the agent request has been received and will be processed:
+
+```http
+HTTP/1.1 200 OK
+Content-Type: text/plain
+x-ms-thread-id: 351ec855-7f4d-4527-a60d-498301ced36d
+
+```
+
+The `x-ms-thread-id` response header contains the thread ID, which can be used to continue the conversation by passing it as a query parameter (`thread_id`) to the `run` endpoint. The commands above show how to save the thread ID to a `$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 +1,93 @@
+// 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}", SanitizeLogValue(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}",
+            SanitizeLogValue(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}", SanitizeLogValue(instanceId));
+
+        // Get the current agent context using the session-static property
+        OrchestrationMetadata? status = await DurableAgentContext.Current.GetOrchestrationStatusAsync(
+            instanceId,
+            includeDetails);
+
+        if (status is null)
+        {
+            this._logger.LogInformation("Workflow instance '{InstanceId}' not found.", SanitizeLogValue(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}", SanitizeLogValue(instanceId));
+        await DurableAgentContext.Current.RaiseOrchestrationEventAsync(instanceId, "HumanApproval", feedback);
+    }
+
+    /// <summary>
+    /// Sanitizes a user-provided value for safe inclusion in log entries
+    /// by removing control characters that could be used for log forging.
+    /// </summary>
+    private static string SanitizeLogValue(string value) =>
+        value
+            .Replace("\r", string.Empty, StringComparison.Ordinal)
+            .Replace("\n", string.Empty, StringComparison.Ordinal);
+}
@@ -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 +1,42 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <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 +1,58 @@
+// 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.
+
+#pragma warning disable IDE0002 // Simplify Member Access
+
+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.Chat;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// Define three AI agents we are going to use in this application.
+AIAgent agent1 = client.GetChatClient(deploymentName).AsAIAgent("You are good at telling jokes.", "Joker");
+
+AIAgent agent2 = client.GetChatClient(deploymentName)
+    .AsAIAgent("Check stock prices.", "StockAdvisor");
+
+AIAgent agent3 = client.GetChatClient(deploymentName)
+    .AsAIAgent("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_NAME": "your-deployment-name",
+    "AZURE_OPENAI_API_KEY": "your-api-key-if-not-using-rbac"
+  }
+}
+```
+
+## Running the Sample
+
+1. **Start the Function App**:
+
+   ```bash
+   cd dotnet/samples/04-hosting/DurableAgents/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 +1,47 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <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>ReliableStreaming</AssemblyName>
+    <RootNamespace>ReliableStreaming</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>
+
+  <!-- Redis for reliable streaming -->
+  <ItemGroup>
+    <PackageReference Include="StackExchange.Redis" />
+  </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 +1,335 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Text;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.Agents.AI.Hosting.AzureFunctions;
+using Microsoft.AspNetCore.Http;
+using Microsoft.AspNetCore.Http.Features;
+using Microsoft.AspNetCore.Mvc;
+using Microsoft.Azure.Functions.Worker;
+using Microsoft.DurableTask.Client;
+using Microsoft.Extensions.Logging;
+
+namespace ReliableStreaming;
+
+/// <summary>
+/// HTTP trigger functions for reliable streaming of durable agent responses.
+/// </summary>
+/// <remarks>
+/// This class exposes two endpoints:
+/// <list type="bullet">
+/// <item>
+/// <term>Create</term>
+/// <description>Starts an agent run and streams responses. The response format depends on the
+/// <c>Accept</c> header: <c>text/plain</c> returns raw text (ideal for terminals), while
+/// <c>text/event-stream</c> or any other value returns Server-Sent Events (SSE).</description>
+/// </item>
+/// <item>
+/// <term>Stream</term>
+/// <description>Resumes a stream from a cursor position, enabling reliable message delivery</description>
+/// </item>
+/// </list>
+/// </remarks>
+public sealed class FunctionTriggers
+{
+    private readonly RedisStreamResponseHandler _streamHandler;
+    private readonly ILogger<FunctionTriggers> _logger;
+
+    /// <summary>
+    /// Initializes a new instance of the <see cref="FunctionTriggers"/> class.
+    /// </summary>
+    /// <param name="streamHandler">The Redis stream handler for reading/writing agent responses.</param>
+    /// <param name="logger">The logger instance.</param>
+    public FunctionTriggers(RedisStreamResponseHandler streamHandler, ILogger<FunctionTriggers> logger)
+    {
+        this._streamHandler = streamHandler;
+        this._logger = logger;
+    }
+
+    /// <summary>
+    /// Creates a new agent session, starts an agent run with the provided prompt,
+    /// and streams the response back to the client.
+    /// </summary>
+    /// <remarks>
+    /// <para>
+    /// The response format depends on the <c>Accept</c> header:
+    /// <list type="bullet">
+    /// <item><c>text/plain</c>: Returns raw text output, ideal for terminal display with curl</item>
+    /// <item><c>text/event-stream</c> or other: Returns Server-Sent Events (SSE) with cursor support</item>
+    /// </list>
+    /// </para>
+    /// <para>
+    /// The response includes an <c>x-conversation-id</c> header containing the conversation ID.
+    /// For SSE responses, clients can use this conversation ID to resume the stream if disconnected
+    /// by calling the <see cref="StreamAsync"/> endpoint with the conversation ID and the last received cursor.
+    /// </para>
+    /// <para>
+    /// Each SSE event contains the following fields:
+    /// <list type="bullet">
+    /// <item><c>id</c>: The Redis stream entry ID (use as cursor for resumption)</item>
+    /// <item><c>event</c>: Either "message" for content or "done" for stream completion</item>
+    /// <item><c>data</c>: The text content of the response chunk</item>
+    /// </list>
+    /// </para>
+    /// </remarks>
+    /// <param name="request">The HTTP request containing the prompt in the body.</param>
+    /// <param name="durableClient">The Durable Task client for signaling agents.</param>
+    /// <param name="context">The function invocation context.</param>
+    /// <param name="cancellationToken">Cancellation token.</param>
+    /// <returns>A streaming response in the format specified by the Accept header.</returns>
+    [Function(nameof(CreateAsync))]
+    public async Task<IActionResult> CreateAsync(
+        [HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = "agent/create")] HttpRequest request,
+        [DurableClient] DurableTaskClient durableClient,
+        FunctionContext context,
+        CancellationToken cancellationToken)
+    {
+        // Read the prompt from the request body
+        string prompt = await new StreamReader(request.Body).ReadToEndAsync(cancellationToken);
+        if (string.IsNullOrWhiteSpace(prompt))
+        {
+            return new BadRequestObjectResult("Request body must contain a prompt.");
+        }
+
+        AIAgent agentProxy = durableClient.AsDurableAgentProxy(context, "TravelPlanner");
+
+        // Create a new agent session
+        AgentSession session = await agentProxy.CreateSessionAsync(cancellationToken);
+        string agentSessionId = session.GetService<AgentSessionId>().ToString();
+
+        this._logger.LogInformation("Creating new agent session: {AgentSessionId}", agentSessionId);
+
+        // Run the agent in the background (fire-and-forget)
+        DurableAgentRunOptions options = new() { IsFireAndForget = true };
+        await agentProxy.RunAsync(prompt, session, options, cancellationToken);
+
+        this._logger.LogInformation("Agent run started for session: {AgentSessionId}", agentSessionId);
+
+        // Check Accept header to determine response format
+        // text/plain = raw text output (ideal for terminals)
+        // text/event-stream or other = SSE format (supports resumption)
+        string? acceptHeader = request.Headers.Accept.FirstOrDefault();
+        bool useSseFormat = acceptHeader?.Contains("text/plain", StringComparison.OrdinalIgnoreCase) != true;
+
+        return await this.StreamToClientAsync(
+            conversationId: agentSessionId, cursor: null, useSseFormat, request.HttpContext, cancellationToken);
+    }
+
+    /// <summary>
+    /// Resumes streaming from a specific cursor position for an existing session.
+    /// </summary>
+    /// <remarks>
+    /// <para>
+    /// Use this endpoint to resume a stream after disconnection. Pass the conversation ID
+    /// (from the <c>x-conversation-id</c> response header) and the last received cursor
+    /// (Redis stream entry ID) to continue from where you left off.
+    /// </para>
+    /// <para>
+    /// If no cursor is provided, streaming starts from the beginning of the stream.
+    /// This allows clients to replay the entire response if needed.
+    /// </para>
+    /// <para>
+    /// The response format depends on the <c>Accept</c> header:
+    /// <list type="bullet">
+    /// <item><c>text/plain</c>: Returns raw text output, ideal for terminal display with curl</item>
+    /// <item><c>text/event-stream</c> or other: Returns Server-Sent Events (SSE) with cursor support</item>
+    /// </list>
+    /// </para>
+    /// </remarks>
+    /// <param name="request">The HTTP request. Use the <c>cursor</c> query parameter to specify the cursor position.</param>
+    /// <param name="conversationId">The conversation ID to stream from.</param>
+    /// <param name="cancellationToken">Cancellation token.</param>
+    /// <returns>A streaming response in the format specified by the Accept header.</returns>
+    [Function(nameof(StreamAsync))]
+    public async Task<IActionResult> StreamAsync(
+        [HttpTrigger(AuthorizationLevel.Anonymous, "get", Route = "agent/stream/{conversationId}")] HttpRequest request,
+        string conversationId,
+        CancellationToken cancellationToken)
+    {
+        if (string.IsNullOrWhiteSpace(conversationId))
+        {
+            return new BadRequestObjectResult("Conversation ID is required.");
+        }
+
+        // Get the cursor from query string (optional)
+        string? cursor = request.Query["cursor"].FirstOrDefault();
+
+        this._logger.LogInformation(
+            "Resuming stream for conversation {ConversationId} from cursor: {Cursor}",
+            SanitizeLogValue(conversationId),
+            SanitizeLogValue(cursor) ?? "(beginning)");
+
+        // Check Accept header to determine response format
+        // text/plain = raw text output (ideal for terminals)
+        // text/event-stream or other = SSE format (supports cursor-based resumption)
+        string? acceptHeader = request.Headers.Accept.FirstOrDefault();
+        bool useSseFormat = acceptHeader?.Contains("text/plain", StringComparison.OrdinalIgnoreCase) != true;
+
+        return await this.StreamToClientAsync(conversationId, cursor, useSseFormat, request.HttpContext, cancellationToken);
+    }
+
+    /// <summary>
+    /// Streams chunks from the Redis stream to the HTTP response.
+    /// </summary>
+    /// <param name="conversationId">The conversation ID to stream from.</param>
+    /// <param name="cursor">Optional cursor to resume from. If null, streams from the beginning.</param>
+    /// <param name="useSseFormat">True to use SSE format, false for plain text.</param>
+    /// <param name="httpContext">The HTTP context for writing the response.</param>
+    /// <param name="cancellationToken">Cancellation token.</param>
+    /// <returns>An empty result after streaming completes.</returns>
+    private async Task<IActionResult> StreamToClientAsync(
+        string conversationId,
+        string? cursor,
+        bool useSseFormat,
+        HttpContext httpContext,
+        CancellationToken cancellationToken)
+    {
+        // Set response headers based on format
+        httpContext.Response.Headers.ContentType = useSseFormat
+            ? "text/event-stream"
+            : "text/plain; charset=utf-8";
+        httpContext.Response.Headers.CacheControl = "no-cache";
+        httpContext.Response.Headers.Connection = "keep-alive";
+        httpContext.Response.Headers["x-conversation-id"] = conversationId;
+
+        // Disable response buffering if supported
+        httpContext.Features.Get<IHttpResponseBodyFeature>()?.DisableBuffering();
+
+        try
+        {
+            await foreach (StreamChunk chunk in this._streamHandler.ReadStreamAsync(
+                conversationId,
+                cursor,
+                cancellationToken))
+            {
+                if (chunk.Error != null)
+                {
+                    this._logger.LogWarning("Stream error for conversation {ConversationId}: {Error}", SanitizeLogValue(conversationId), chunk.Error);
+                    await WriteErrorAsync(httpContext.Response, chunk.Error, useSseFormat, cancellationToken);
+                    break;
+                }
+
+                if (chunk.IsDone)
+                {
+                    await WriteEndOfStreamAsync(httpContext.Response, chunk.EntryId, useSseFormat, cancellationToken);
+                    break;
+                }
+
+                if (chunk.Text != null)
+                {
+                    await WriteChunkAsync(httpContext.Response, chunk, useSseFormat, cancellationToken);
+                }
+            }
+        }
+        catch (OperationCanceledException)
+        {
+            this._logger.LogInformation("Client disconnected from stream {ConversationId}", SanitizeLogValue(conversationId));
+        }
+
+        return new EmptyResult();
+    }
+
+    /// <summary>
+    /// Writes a text chunk to the response.
+    /// </summary>
+    private static async Task WriteChunkAsync(
+        HttpResponse response,
+        StreamChunk chunk,
+        bool useSseFormat,
+        CancellationToken cancellationToken)
+    {
+        if (useSseFormat)
+        {
+            await WriteSSEEventAsync(response, "message", chunk.Text!, chunk.EntryId);
+        }
+        else
+        {
+            await response.WriteAsync(chunk.Text!, cancellationToken);
+        }
+
+        await response.Body.FlushAsync(cancellationToken);
+    }
+
+    /// <summary>
+    /// Writes an end-of-stream marker to the response.
+    /// </summary>
+    private static async Task WriteEndOfStreamAsync(
+        HttpResponse response,
+        string entryId,
+        bool useSseFormat,
+        CancellationToken cancellationToken)
+    {
+        if (useSseFormat)
+        {
+            await WriteSSEEventAsync(response, "done", "[DONE]", entryId);
+        }
+        else
+        {
+            await response.WriteAsync("\n", cancellationToken);
+        }
+
+        await response.Body.FlushAsync(cancellationToken);
+    }
+
+    /// <summary>
+    /// Writes an error message to the response.
+    /// </summary>
+    private static async Task WriteErrorAsync(
+        HttpResponse response,
+        string error,
+        bool useSseFormat,
+        CancellationToken cancellationToken)
+    {
+        if (useSseFormat)
+        {
+            await WriteSSEEventAsync(response, "error", error, null);
+        }
+        else
+        {
+            await response.WriteAsync($"\n[Error: {error}]\n", cancellationToken);
+        }
+
+        await response.Body.FlushAsync(cancellationToken);
+    }
+
+    /// <summary>
+    /// Writes a Server-Sent Event to the response stream.
+    /// </summary>
+    private static async Task WriteSSEEventAsync(
+        HttpResponse response,
+        string eventType,
+        string data,
+        string? id)
+    {
+        StringBuilder sb = new();
+
+        // Include the ID if provided (used as cursor for resumption)
+        if (!string.IsNullOrEmpty(id))
+        {
+            sb.AppendLine($"id: {id}");
+        }
+
+        sb.AppendLine($"event: {eventType}");
+        sb.AppendLine($"data: {data}");
+        sb.AppendLine(); // Empty line marks end of event
+
+        await response.WriteAsync(sb.ToString());
+    }
+
+    /// <summary>
+    /// Sanitizes a user-provided value for safe inclusion in log entries
+    /// by removing control characters that could be used for log forging.
+    /// </summary>
+    private static string? SanitizeLogValue(string? value)
+    {
+        if (value is null)
+        {
+            return null;
+        }
+
+        return value
+            .Replace("\r", string.Empty, StringComparison.Ordinal)
+            .Replace("\n", string.Empty, StringComparison.Ordinal);
+    }
+}
@@ -0,0 +1,105 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to implement reliable streaming for durable agents using Redis Streams.
+// It exposes two HTTP endpoints:
+// 1. Create - Starts an agent run and streams responses back via Server-Sent Events (SSE)
+// 2. Stream - Resumes a stream from a specific cursor position, enabling reliable message delivery
+//
+// This pattern is inspired by OpenAI's background mode for the Responses API, which allows clients
+// to disconnect and reconnect to ongoing agent responses without losing messages.
+
+#pragma warning disable IDE0002 // Simplify Member Access
+
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI.DurableTask;
+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 OpenAI.Chat;
+using ReliableStreaming;
+using StackExchange.Redis;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Get Redis connection string from environment variable.
+string redisConnectionString = Environment.GetEnvironmentVariable("REDIS_CONNECTION_STRING")
+    ?? "localhost:6379";
+
+// Get the Redis stream TTL from environment variable (default: 10 minutes).
+int redisStreamTtlMinutes = int.TryParse(
+    Environment.GetEnvironmentVariable("REDIS_STREAM_TTL_MINUTES"),
+    out int ttlMinutes) ? ttlMinutes : 10;
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = System.Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// Travel Planner agent instructions - designed to produce longer responses for demonstrating streaming.
+const string TravelPlannerName = "TravelPlanner";
+const string TravelPlannerInstructions =
+    """
+    You are an expert travel planner who creates detailed, personalized travel itineraries.
+    When asked to plan a trip, you should:
+    1. Create a comprehensive day-by-day itinerary
+    2. Include specific recommendations for activities, restaurants, and attractions
+    3. Provide practical tips for each destination
+    4. Consider weather and local events when making recommendations
+    5. Include estimated times and logistics between activities
+    
+    Always use the available tools to get current weather forecasts and local events
+    for the destination to make your recommendations more relevant and timely.
+    
+    Format your response with clear headings for each day and include emoji icons
+    to make the itinerary easy to scan and visually appealing.
+    """;
+
+// Configure the function app to host the AI agent.
+FunctionsApplicationBuilder builder = FunctionsApplication
+    .CreateBuilder(args)
+    .ConfigureFunctionsWebApplication()
+    .ConfigureDurableAgents(options =>
+    {
+        // Define the Travel Planner agent with tools for weather and events
+        options.AddAIAgentFactory(TravelPlannerName, sp =>
+        {
+            return client.GetChatClient(deploymentName).AsAIAgent(
+                instructions: TravelPlannerInstructions,
+                name: TravelPlannerName,
+                services: sp,
+                tools: [
+                    AIFunctionFactory.Create(TravelTools.GetWeatherForecast),
+                    AIFunctionFactory.Create(TravelTools.GetLocalEvents),
+                ]);
+        });
+    });
+
+// Register Redis connection as a singleton
+builder.Services.AddSingleton<IConnectionMultiplexer>(_ =>
+    ConnectionMultiplexer.Connect(redisConnectionString));
+
+// Register the Redis stream response handler - this captures agent responses
+// and publishes them to Redis Streams for reliable delivery.
+// Registered as both the concrete type (for FunctionTriggers) and the interface (for the agent framework).
+builder.Services.AddSingleton(sp =>
+    new RedisStreamResponseHandler(
+        sp.GetRequiredService<IConnectionMultiplexer>(),
+        TimeSpan.FromMinutes(redisStreamTtlMinutes)));
+builder.Services.AddSingleton<IAgentResponseHandler>(sp =>
+    sp.GetRequiredService<RedisStreamResponseHandler>());
+
+using IHost app = builder.Build();
+
+app.Run();
@@ -0,0 +1,264 @@
+# Reliable Streaming with Redis
+
+This sample demonstrates how to implement reliable streaming for durable agents using Redis Streams as a message broker. It enables clients to disconnect and reconnect to ongoing agent responses without losing messages, inspired by [OpenAI's background mode](https://platform.openai.com/docs/guides/background) for the Responses API.
+
+## Key Concepts Demonstrated
+
+- **Reliable message delivery**: Agent responses are persisted to Redis Streams, allowing clients to resume from any point
+- **Content negotiation**: Use `Accept: text/plain` for raw terminal output, or `Accept: text/event-stream` for SSE format
+- **Server-Sent Events (SSE)**: Standard streaming format that works with `curl`, browsers, and most HTTP clients
+- **Cursor-based resumption**: Each SSE event includes an `id` field that can be used to resume the stream
+- **Fire-and-forget agent invocation**: The agent runs in the background while the client streams from Redis via an HTTP trigger function
+
+## 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.
+
+### Additional Requirements: Redis
+
+This sample requires a Redis instance. Start a local Redis instance using Docker:
+
+```bash
+docker run -d --name redis -p 6379:6379 redis:latest
+```
+
+To verify Redis is running:
+
+```bash
+docker ps | grep redis
+```
+
+## Running the Sample
+
+Start the Azure Functions host:
+
+```bash
+func start
+```
+
+### 1. Test Streaming with curl
+
+Open a new terminal and start a travel planning request. Use the `-i` flag to see response headers (including the conversation ID) and `Accept: text/plain` for raw text output:
+
+**Bash (Linux/macOS/WSL):**
+
+```bash
+curl -i -N -X POST http://localhost:7071/api/agent/create \
+  -H "Content-Type: text/plain" \
+  -H "Accept: text/plain" \
+  -d "Plan a 7-day trip to Tokyo, Japan for next month. Include daily activities, restaurant recommendations, and tips for getting around."
+```
+
+**PowerShell:**
+
+```powershell
+curl -i -N -X POST http://localhost:7071/api/agent/create `
+  -H "Content-Type: text/plain" `
+  -H "Accept: text/plain" `
+  -d "Plan a 7-day trip to Tokyo, Japan for next month. Include daily activities, restaurant recommendations, and tips for getting around."
+```
+
+You'll first see the response headers, including:
+
+```text
+HTTP/1.1 200 OK
+Content-Type: text/plain; charset=utf-8
+x-conversation-id: @dafx-travelplanner@a1b2c3d4e5f67890abcdef1234567890
+...
+```
+
+Then the agent's response will stream to your terminal in chunks, similar to a ChatGPT-style experience (though not character-by-character).
+
+> **Note:** The `-N` flag in curl disables output buffering, which is essential for seeing the stream in real-time. The `-i` flag includes the HTTP headers in the output.
+
+### 2. Demonstrate Stream Interruption and Resumption
+
+This is the key feature of reliable streaming! Follow these steps to see it in action:
+
+#### Step 1: Start a stream and note the conversation ID
+
+Run the curl command from step 1. Watch for the `x-conversation-id` header in the response - **copy this value**, you'll need it to resume.
+
+```text
+x-conversation-id: @dafx-travelplanner@a1b2c3d4e5f67890abcdef1234567890
+```
+
+#### Step 2: Interrupt the stream
+
+While the agent is still generating text, press **`Ctrl+C`** to interrupt the stream. The agent continues running in the background - your messages are being saved to Redis!
+
+#### Step 3: Resume the stream
+
+Use the conversation ID you copied to resume streaming from where you left off. Include the `Accept: text/plain` header to get raw text output:
+
+**Bash (Linux/macOS/WSL):**
+
+```bash
+# Replace with your actual conversation ID from the x-conversation-id header
+CONVERSATION_ID="@dafx-travelplanner@a1b2c3d4e5f67890abcdef1234567890"
+
+curl -N -H "Accept: text/plain" "http://localhost:7071/api/agent/stream/${CONVERSATION_ID}"
+```
+
+**PowerShell:**
+
+```powershell
+# Replace with your actual conversation ID from the x-conversation-id header
+$conversationId = "@dafx-travelplanner@a1b2c3d4e5f67890abcdef1234567890"
+
+curl -N -H "Accept: text/plain" "http://localhost:7071/api/agent/stream/$conversationId"
+```
+
+You'll see the **entire response replayed from the beginning**, including the parts you already received before interrupting.
+
+#### Step 4 (Advanced): Resume from a specific cursor
+
+If you're using SSE format, each event includes an `id` field that you can use as a cursor to resume from a specific point:
+
+```bash
+# Resume from a specific cursor position
+curl -N "http://localhost:7071/api/agent/stream/${CONVERSATION_ID}?cursor=1734567890123-0"
+```
+
+### 3. Alternative: SSE Format for Programmatic Clients
+
+If you need the full Server-Sent Events format with cursors for resumable streaming, use `Accept: text/event-stream` (or omit the Accept header):
+
+```bash
+curl -i -N -X POST http://localhost:7071/api/agent/create \
+  -H "Content-Type: text/plain" \
+  -H "Accept: text/event-stream" \
+  -d "Plan a 7-day trip to Tokyo, Japan."
+```
+
+This returns SSE-formatted events with `id`, `event`, and `data` fields:
+
+```text
+id: 1734567890123-0
+event: message
+data: # 7-Day Tokyo Adventure
+
+id: 1734567890124-0
+event: message
+data: ## Day 1: Arrival and Exploration
+
+id: 1734567890999-0
+event: done
+data: [DONE]
+```
+
+The `id` field is the Redis stream entry ID - use it as the `cursor` parameter to resume from that exact point.
+
+### Understanding the Response Headers
+
+| Header | Description |
+|--------|-------------|
+| `x-conversation-id` | The conversation ID (session key). Use this to resume the stream. |
+| `Content-Type` | Either `text/plain` or `text/event-stream` depending on your `Accept` header. |
+| `Cache-Control` | Set to `no-cache` to prevent caching of the stream. |
+
+## Architecture Overview
+
+```text
+┌─────────────┐      POST /agent/create     ┌─────────────────────┐
+│   Client    │  (Accept: text/plain or SSE)│  Azure Functions    │
+│   (curl)    │ ──────────────────────────► │  (FunctionTriggers) │
+└─────────────┘                             └──────────┬──────────┘
+       ▲                                               │
+       │ Text or SSE stream                  Signal Entity
+       │                                               │
+       │                                               ▼
+       │                                    ┌─────────────────────┐
+       │                                    │   AgentEntity       │
+       │                                    │   (Durable Entity)  │
+       │                                    └──────────┬──────────┘
+       │                                               │
+       │                                    IAgentResponseHandler
+       │                                               │
+       │                                               ▼
+       │                                    ┌─────────────────────┐
+       │                                    │ RedisStreamResponse │
+       │                                    │      Handler        │
+       │                                    └──────────┬──────────┘
+       │                                               │
+       │                                     XADD (write)
+       │                                               │
+       │                                               ▼
+       │                                    ┌─────────────────────┐
+       └─────────── XREAD (poll) ────────── │   Redis Streams     │
+                                            │  (Durable Log)      │
+                                            └─────────────────────┘
+```
+
+### Data Flow
+
+1. **Client sends prompt**: The `Create` endpoint receives the prompt and generates a new agent thread.
+
+2. **Agent invoked**: The durable entity (`AgentEntity`) is signaled to run the travel planner agent. This is fire-and-forget from the HTTP request's perspective.
+
+3. **Responses captured**: As the agent generates responses, `RedisStreamResponseHandler` (implementing `IAgentResponseHandler`) extracts the text from each `AgentResponseUpdate` and publishes it to a Redis Stream keyed by session ID.
+
+4. **Client polls Redis**: The HTTP response streams events by polling the Redis Stream. For SSE format, each event includes the Redis entry ID as the `id` field.
+
+5. **Resumption**: If the client disconnects, it can call the `Stream` endpoint with the conversation ID (from the `x-conversation-id` header) and optionally the last received cursor to resume from that point.
+
+## Message Delivery Guarantees
+
+This sample provides **at-least-once delivery** with the following characteristics:
+
+- **Durability**: Messages are persisted to Redis Streams with configurable TTL (default: 10 minutes).
+- **Ordering**: Messages are delivered in order within a session.
+- **Resumption**: Clients can resume from any point using cursor-based pagination.
+- **Replay**: Clients can replay the entire stream by omitting the cursor.
+
+### Important Considerations
+
+- **No exactly-once delivery**: If a client disconnects exactly when receiving a message, it may receive that message again upon resumption. Clients should handle duplicate messages idempotently.
+- **TTL expiration**: Streams expire after the configured TTL. Clients cannot resume streams that have expired.
+- **Redis guarantees**: Redis streams are backed by Redis persistence mechanisms (RDB/AOF). Ensure your Redis instance is configured for durability as needed.
+
+## When to Use These Patterns
+
+The patterns demonstrated in this sample are ideal for:
+
+- **Long-running agent tasks**: When agent responses take minutes to complete (e.g., deep research, complex planning)
+- **Unreliable network connections**: Mobile apps, unstable WiFi, or connections that may drop
+- **Resumable experiences**: Users should be able to close and reopen an app without losing context
+- **Background processing**: When you want to fire off a task and check on it later
+
+These patterns may be overkill for:
+
+- **Simple, fast responses**: If responses complete in a few seconds, standard streaming is simpler
+- **Stateless interactions**: If there's no need to resume or replay conversations
+- **Very high throughput**: Redis adds latency; for maximum throughput, direct streaming may be better
+
+## Configuration
+
+| Environment Variable | Description | Default |
+|---------------------|-------------|---------|
+| `REDIS_CONNECTION_STRING` | Redis connection string | `localhost:6379` |
+| `REDIS_STREAM_TTL_MINUTES` | How long streams are retained after last write | `10` |
+| `AZURE_OPENAI_ENDPOINT` | Azure OpenAI endpoint URL | (required) |
+| `AZURE_OPENAI_DEPLOYMENT_NAME` | Azure OpenAI deployment name | (required) |
+| `AZURE_OPENAI_API_KEY` | API key (optional, uses Azure CLI auth if not set) | (optional) |
+
+## Cleanup
+
+To stop and remove the Redis Docker containers:
+
+```bash
+docker stop redis
+docker rm redis
+```
+
+## Disclaimer
+
+> ⚠️ **This sample is for illustration purposes only and is not intended to be production-ready.**
+>
+> A production implementation should consider:
+>
+> - Redis cluster configuration for high availability
+> - Authentication and authorization for the streaming endpoints
+> - Rate limiting and abuse prevention
+> - Monitoring and alerting for stream health
+> - Graceful handling of Redis failures
@@ -0,0 +1,212 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Runtime.CompilerServices;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using StackExchange.Redis;
+
+namespace ReliableStreaming;
+
+/// <summary>
+/// Represents a chunk of data read from a Redis stream.
+/// </summary>
+/// <param name="EntryId">The Redis stream entry ID (can be used as a cursor for resumption).</param>
+/// <param name="Text">The text content of the chunk, or null if this is a completion/error marker.</param>
+/// <param name="IsDone">True if this chunk marks the end of the stream.</param>
+/// <param name="Error">An error message if something went wrong, or null otherwise.</param>
+public readonly record struct StreamChunk(string EntryId, string? Text, bool IsDone, string? Error);
+
+/// <summary>
+/// An implementation of <see cref="IAgentResponseHandler"/> that publishes agent response updates
+/// to Redis Streams for reliable delivery. This enables clients to disconnect and reconnect
+/// to ongoing agent responses without losing messages.
+/// </summary>
+/// <remarks>
+/// <para>
+/// Redis Streams provide a durable, append-only log that supports consumer groups and message
+/// acknowledgment. This implementation uses auto-generated IDs (which are timestamp-based)
+/// as sequence numbers, allowing clients to resume from any point in the stream.
+/// </para>
+/// <para>
+/// Each agent session gets its own Redis Stream, keyed by session ID. The stream entries
+/// contain text chunks extracted from <see cref="AgentResponseUpdate"/> objects.
+/// </para>
+/// </remarks>
+public sealed class RedisStreamResponseHandler : IAgentResponseHandler
+{
+    private const int MaxEmptyReads = 300; // 5 minutes at 1 second intervals
+    private const int PollIntervalMs = 1000;
+
+    private readonly IConnectionMultiplexer _redis;
+    private readonly TimeSpan _streamTtl;
+
+    /// <summary>
+    /// Initializes a new instance of the <see cref="RedisStreamResponseHandler" /> class.
+    /// </summary>
+    /// <param name="redis">The Redis connection multiplexer.</param>
+    /// <param name="streamTtl">The time-to-live for stream entries. Streams will expire after this duration of inactivity.</param>
+    public RedisStreamResponseHandler(IConnectionMultiplexer redis, TimeSpan streamTtl)
+    {
+        this._redis = redis;
+        this._streamTtl = streamTtl;
+    }
+
+    /// <inheritdoc/>
+    public async ValueTask OnStreamingResponseUpdateAsync(
+        IAsyncEnumerable<AgentResponseUpdate> messageStream,
+        CancellationToken cancellationToken)
+    {
+        // Get the current session ID from the DurableAgentContext
+        // This is set by the AgentEntity before invoking the response handler
+        DurableAgentContext? context = DurableAgentContext.Current;
+        if (context is null)
+        {
+            throw new InvalidOperationException(
+                "DurableAgentContext.Current is not set. This handler must be used within a durable agent context.");
+        }
+
+        // Get session ID from the current session context, which is only available in the context of
+        // a durable agent execution.
+        string agentSessionId = context.CurrentSession.GetService<AgentSessionId>().ToString();
+        string streamKey = GetStreamKey(agentSessionId);
+
+        IDatabase db = this._redis.GetDatabase();
+        int sequenceNumber = 0;
+
+        await foreach (AgentResponseUpdate update in messageStream.WithCancellation(cancellationToken))
+        {
+            // Extract just the text content - this avoids serialization round-trip issues
+            string text = update.Text;
+
+            // Only publish non-empty text chunks
+            if (!string.IsNullOrEmpty(text))
+            {
+                // Create the stream entry with the text and metadata
+                NameValueEntry[] entries =
+                [
+                    new NameValueEntry("text", text),
+                    new NameValueEntry("sequence", sequenceNumber++),
+                    new NameValueEntry("timestamp", DateTimeOffset.UtcNow.ToUnixTimeMilliseconds()),
+                ];
+
+                // Add to the Redis Stream with auto-generated ID (timestamp-based)
+                await db.StreamAddAsync(streamKey, entries);
+
+                // Refresh the TTL on each write to keep the stream alive during active streaming
+                await db.KeyExpireAsync(streamKey, this._streamTtl);
+            }
+        }
+
+        // Add a sentinel entry to mark the end of the stream
+        NameValueEntry[] endEntries =
+        [
+            new NameValueEntry("text", ""),
+            new NameValueEntry("sequence", sequenceNumber),
+            new NameValueEntry("timestamp", DateTimeOffset.UtcNow.ToUnixTimeMilliseconds()),
+            new NameValueEntry("done", "true"),
+        ];
+        await db.StreamAddAsync(streamKey, endEntries);
+
+        // Set final TTL - the stream will be cleaned up after this duration
+        await db.KeyExpireAsync(streamKey, this._streamTtl);
+    }
+
+    /// <inheritdoc/>
+    public ValueTask OnAgentResponseAsync(AgentResponse message, CancellationToken cancellationToken)
+    {
+        // This handler is optimized for streaming responses.
+        // For non-streaming responses, we don't need to store in Redis since
+        // the response is returned directly to the caller.
+        return ValueTask.CompletedTask;
+    }
+
+    /// <summary>
+    /// Reads chunks from a Redis stream for the given session, yielding them as they become available.
+    /// </summary>
+    /// <param name="conversationId">The conversation ID to read from.</param>
+    /// <param name="cursor">Optional cursor to resume from. If null, reads from the beginning.</param>
+    /// <param name="cancellationToken">Cancellation token.</param>
+    /// <returns>An async enumerable of stream chunks.</returns>
+    public async IAsyncEnumerable<StreamChunk> ReadStreamAsync(
+        string conversationId,
+        string? cursor,
+        [EnumeratorCancellation] CancellationToken cancellationToken)
+    {
+        string streamKey = GetStreamKey(conversationId);
+
+        IDatabase db = this._redis.GetDatabase();
+        string startId = string.IsNullOrEmpty(cursor) ? "0-0" : cursor;
+
+        int emptyReadCount = 0;
+        bool hasSeenData = false;
+
+        while (!cancellationToken.IsCancellationRequested)
+        {
+            StreamEntry[]? entries = null;
+            string? errorMessage = null;
+
+            try
+            {
+                entries = await db.StreamReadAsync(streamKey, startId, count: 100);
+            }
+            catch (Exception ex)
+            {
+                errorMessage = ex.Message;
+            }
+
+            if (errorMessage != null)
+            {
+                yield return new StreamChunk(startId, null, false, errorMessage);
+                yield break;
+            }
+
+            // entries is guaranteed to be non-null if errorMessage is null
+            if (entries!.Length == 0)
+            {
+                if (!hasSeenData)
+                {
+                    emptyReadCount++;
+                    if (emptyReadCount >= MaxEmptyReads)
+                    {
+                        yield return new StreamChunk(
+                            startId,
+                            null,
+                            false,
+                            $"Stream not found or timed out after {MaxEmptyReads * PollIntervalMs / 1000} seconds");
+                        yield break;
+                    }
+                }
+
+                await Task.Delay(PollIntervalMs, cancellationToken);
+                continue;
+            }
+
+            hasSeenData = true;
+
+            foreach (StreamEntry entry in entries)
+            {
+                startId = entry.Id.ToString();
+                string? text = entry["text"];
+                string? done = entry["done"];
+
+                if (done == "true")
+                {
+                    yield return new StreamChunk(startId, null, true, null);
+                    yield break;
+                }
+
+                if (!string.IsNullOrEmpty(text))
+                {
+                    yield return new StreamChunk(startId, text, false, null);
+                }
+            }
+        }
+    }
+
+    /// <summary>
+    /// Gets the Redis Stream key for a given conversation ID.
+    /// </summary>
+    /// <param name="conversationId">The conversation ID.</param>
+    /// <returns>The Redis Stream key.</returns>
+    internal static string GetStreamKey(string conversationId) => $"agent-stream:{conversationId}";
+}
@@ -0,0 +1,161 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.ComponentModel;
+
+namespace ReliableStreaming;
+
+/// <summary>
+/// Mock travel tools that return hardcoded data for demonstration purposes.
+/// In a real application, these would call actual weather and events APIs.
+/// </summary>
+internal static class TravelTools
+{
+    /// <summary>
+    /// Gets a weather forecast for a destination on a specific date.
+    /// Returns mock weather data for demonstration purposes.
+    /// </summary>
+    /// <param name="destination">The destination city or location.</param>
+    /// <param name="date">The date for the forecast (e.g., "2025-01-15" or "next Monday").</param>
+    /// <returns>A weather forecast summary.</returns>
+    [Description("Gets the weather forecast for a destination on a specific date. Use this to provide weather-aware recommendations in the itinerary.")]
+    public static string GetWeatherForecast(string destination, string date)
+    {
+        // Mock weather data based on destination for realistic responses
+        Dictionary<string, (string condition, int highF, int lowF)> weatherByRegion = new(StringComparer.OrdinalIgnoreCase)
+        {
+            ["Tokyo"] = ("Partly cloudy with a chance of light rain", 58, 45),
+            ["Paris"] = ("Overcast with occasional drizzle", 52, 41),
+            ["New York"] = ("Clear and cold", 42, 28),
+            ["London"] = ("Foggy morning, clearing in afternoon", 48, 38),
+            ["Sydney"] = ("Sunny and warm", 82, 68),
+            ["Rome"] = ("Sunny with light breeze", 62, 48),
+            ["Barcelona"] = ("Partly sunny", 59, 47),
+            ["Amsterdam"] = ("Cloudy with light rain", 46, 38),
+            ["Dubai"] = ("Sunny and hot", 85, 72),
+            ["Singapore"] = ("Tropical thunderstorms in afternoon", 88, 77),
+            ["Bangkok"] = ("Hot and humid, afternoon showers", 91, 78),
+            ["Los Angeles"] = ("Sunny and pleasant", 72, 55),
+            ["San Francisco"] = ("Morning fog, afternoon sun", 62, 52),
+            ["Seattle"] = ("Rainy with breaks", 48, 40),
+            ["Miami"] = ("Warm and sunny", 78, 65),
+            ["Honolulu"] = ("Tropical paradise weather", 82, 72),
+        };
+
+        // Find a matching destination or use a default
+        (string condition, int highF, int lowF) forecast = ("Partly cloudy", 65, 50);
+        foreach (KeyValuePair<string, (string, int, int)> entry in weatherByRegion)
+        {
+            if (destination.Contains(entry.Key, StringComparison.OrdinalIgnoreCase))
+            {
+                forecast = entry.Value;
+                break;
+            }
+        }
+
+        return $"""
+            Weather forecast for {destination} on {date}:
+            Conditions: {forecast.condition}
+            High: {forecast.highF}°F ({(forecast.highF - 32) * 5 / 9}°C)
+            Low: {forecast.lowF}°F ({(forecast.lowF - 32) * 5 / 9}°C)
+            
+            Recommendation: {GetWeatherRecommendation(forecast.condition)}
+            """;
+    }
+
+    /// <summary>
+    /// Gets local events happening at a destination around a specific date.
+    /// Returns mock event data for demonstration purposes.
+    /// </summary>
+    /// <param name="destination">The destination city or location.</param>
+    /// <param name="date">The date to search for events (e.g., "2025-01-15" or "next week").</param>
+    /// <returns>A list of local events and activities.</returns>
+    [Description("Gets local events and activities happening at a destination around a specific date. Use this to suggest timely activities and experiences.")]
+    public static string GetLocalEvents(string destination, string date)
+    {
+        // Mock events data based on destination
+        Dictionary<string, string[]> eventsByCity = new(StringComparer.OrdinalIgnoreCase)
+        {
+            ["Tokyo"] = [
+                "🎭 Kabuki Theater Performance at Kabukiza Theatre - Traditional Japanese drama",
+                "🌸 Winter Illuminations at Yoyogi Park - Spectacular light displays",
+                "🍜 Ramen Festival at Tokyo Station - Sample ramen from across Japan",
+                "🎮 Gaming Expo at Tokyo Big Sight - Latest video games and technology",
+            ],
+            ["Paris"] = [
+                "🎨 Impressionist Exhibition at Musée d'Orsay - Extended evening hours",
+                "🍷 Wine Tasting Tour in Le Marais - Local sommelier guided",
+                "🎵 Jazz Night at Le Caveau de la Huchette - Historic jazz club",
+                "🥐 French Pastry Workshop - Learn from master pâtissiers",
+            ],
+            ["New York"] = [
+                "🎭 Broadway Show: Hamilton - Limited engagement performances",
+                "🏀 Knicks vs Lakers at Madison Square Garden",
+                "🎨 Modern Art Exhibit at MoMA - New installations",
+                "🍕 Pizza Walking Tour of Brooklyn - Artisan pizzerias",
+            ],
+            ["London"] = [
+                "👑 Royal Collection Exhibition at Buckingham Palace",
+                "🎭 West End Musical: The Phantom of the Opera",
+                "🍺 Craft Beer Festival at Brick Lane",
+                "🎪 Winter Wonderland at Hyde Park - Rides and markets",
+            ],
+            ["Sydney"] = [
+                "🏄 Pro Surfing Competition at Bondi Beach",
+                "🎵 Opera at Sydney Opera House - La Bohème",
+                "🦘 Wildlife Night Safari at Taronga Zoo",
+                "🍽️ Harbor Dinner Cruise with fireworks",
+            ],
+            ["Rome"] = [
+                "🏛️ After-Hours Vatican Tour - Skip the crowds",
+                "🍝 Pasta Making Class in Trastevere",
+                "🎵 Classical Concert at Borghese Gallery",
+                "🍷 Wine Tasting in Roman Cellars",
+            ],
+        };
+
+        // Find events for the destination or use generic events
+        string[] events = [
+            "🎭 Local theater performance",
+            "🍽️ Food and wine festival",
+            "🎨 Art gallery opening",
+            "🎵 Live music at local venues",
+        ];
+
+        foreach (KeyValuePair<string, string[]> entry in eventsByCity)
+        {
+            if (destination.Contains(entry.Key, StringComparison.OrdinalIgnoreCase))
+            {
+                events = entry.Value;
+                break;
+            }
+        }
+
+        string eventList = string.Join("\n• ", events);
+        return $"""
+            Local events in {destination} around {date}:
+            
+            • {eventList}
+            
+            💡 Tip: Book popular events in advance as they may sell out quickly!
+            """;
+    }
+
+    private static string GetWeatherRecommendation(string condition)
+    {
+        // Use case-insensitive comparison instead of ToLowerInvariant() to satisfy CA1308
+        return condition switch
+        {
+            string c when c.Contains("rain", StringComparison.OrdinalIgnoreCase) || c.Contains("drizzle", StringComparison.OrdinalIgnoreCase) =>
+                "Bring an umbrella and waterproof jacket. Consider indoor activities for backup.",
+            string c when c.Contains("fog", StringComparison.OrdinalIgnoreCase) =>
+                "Morning visibility may be limited. Plan outdoor sightseeing for afternoon.",
+            string c when c.Contains("cold", StringComparison.OrdinalIgnoreCase) =>
+                "Layer up with warm clothing. Hot drinks and cozy cafés recommended.",
+            string c when c.Contains("hot", StringComparison.OrdinalIgnoreCase) || c.Contains("warm", StringComparison.OrdinalIgnoreCase) =>
+                "Stay hydrated and use sunscreen. Plan strenuous activities for cooler morning hours.",
+            string c when c.Contains("thunder", StringComparison.OrdinalIgnoreCase) || c.Contains("storm", StringComparison.OrdinalIgnoreCase) =>
+                "Keep an eye on weather updates. Have indoor alternatives ready.",
+            _ => "Pleasant conditions expected. Great day for outdoor exploration!"
+        };
+    }
+}
@@ -0,0 +1,21 @@
+{
+  "version": "2.0",
+  "logging": {
+    "logLevel": {
+      "Microsoft.Agents.AI.DurableTask": "Information",
+      "Microsoft.Agents.AI.Hosting.AzureFunctions": "Information",
+      "DurableTask": "Information",
+      "Microsoft.DurableTask": "Information",
+      "ReliableStreaming": "Information"
+    }
+  },
+  "extensions": {
+    "durableTask": {
+      "hubName": "default",
+      "storageProvider": {
+        "type": "AzureManaged",
+        "connectionStringName": "DURABLE_TASK_SCHEDULER_CONNECTION_STRING"
+      }
+    }
+  }
+}
@@ -0,0 +1,152 @@
+# 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.
+- **[08_ReliableStreaming](08_ReliableStreaming)**: A sample that demonstrates how to implement reliable streaming for durable agents using Redis Streams, enabling clients to disconnect and reconnect without losing messages.
+
+## 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 10.0 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-5.4-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_API_KEY` environment variable.
+
+Bash (Linux/macOS/WSL):
+
+```bash
+export AZURE_OPENAI_API_KEY="your-api-key"
+```
+
+PowerShell:
+
+```powershell
+$env:AZURE_OPENAI_API_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_NAME": "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_NAME="your-deployment-name"
+```
+
+### PowerShell
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="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/04-hosting/DurableAgents/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 +1,30 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <AssemblyName>SingleAgent</AssemblyName>
+    <RootNamespace>SingleAgent</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.DurableTask.Client.AzureManaged" />
+    <PackageReference Include="Microsoft.DurableTask.Worker.AzureManaged" />
+    <PackageReference Include="Microsoft.Extensions.Hosting" />
+  </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.DurableTask" />
+    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.DurableTask\Microsoft.Agents.AI.DurableTask.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+</Project>
@@ -0,0 +1,106 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.DurableTask.Client.AzureManaged;
+using Microsoft.DurableTask.Worker.AzureManaged;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+using Microsoft.Extensions.Logging;
+using OpenAI.Chat;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Get DTS connection string from environment variable
+string dtsConnectionString = Environment.GetEnvironmentVariable("DURABLE_TASK_SCHEDULER_CONNECTION_STRING")
+    ?? "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None";
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// 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).AsAIAgent(JokerInstructions, JokerName);
+
+// Configure the console app to host the AI agent.
+IHost host = Host.CreateDefaultBuilder(args)
+    .ConfigureLogging(logging => logging.SetMinimumLevel(LogLevel.Warning))
+    .ConfigureServices(services =>
+    {
+        services.ConfigureDurableAgents(
+            options => options.AddAIAgent(agent, timeToLive: TimeSpan.FromHours(1)),
+            workerBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString),
+            clientBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString));
+    })
+    .Build();
+
+await host.StartAsync();
+
+// Get the agent proxy from services
+IServiceProvider services = host.Services;
+AIAgent agentProxy = services.GetRequiredKeyedService<AIAgent>(JokerName);
+
+// Console colors for better UX
+Console.ForegroundColor = ConsoleColor.Cyan;
+Console.WriteLine("=== Single Agent Console Sample ===");
+Console.ResetColor();
+Console.WriteLine("Enter a message for the Joker agent (or 'exit' to quit):");
+Console.WriteLine();
+
+// Create a session for the conversation
+AgentSession session = await agentProxy.CreateSessionAsync();
+
+while (true)
+{
+    // Read input from stdin
+    Console.ForegroundColor = ConsoleColor.Yellow;
+    Console.Write("You: ");
+    Console.ResetColor();
+
+    string? input = Console.ReadLine();
+    if (string.IsNullOrWhiteSpace(input) || input.Equals("exit", StringComparison.OrdinalIgnoreCase))
+    {
+        break;
+    }
+
+    // Run the agent
+    Console.ForegroundColor = ConsoleColor.Green;
+    Console.Write("Joker: ");
+    Console.ResetColor();
+
+    try
+    {
+        AgentResponse agentResponse = await agentProxy.RunAsync(
+            message: input,
+            session: session,
+            cancellationToken: CancellationToken.None);
+
+        Console.WriteLine(agentResponse.Text);
+        Console.WriteLine();
+    }
+    catch (Exception ex)
+    {
+        Console.ForegroundColor = ConsoleColor.Red;
+        Console.Error.WriteLine($"Error: {ex.Message}");
+        Console.ResetColor();
+        Console.WriteLine();
+    }
+}
+
+await host.StopAsync();
@@ -0,0 +1,56 @@
+# Single Agent Sample
+
+This sample demonstrates how to use the durable agents extension to create a simple console app that hosts a single AI agent and provides interactive conversation via stdin/stdout.
+
+## Key Concepts Demonstrated
+
+- Using the Microsoft Agent Framework to define a simple AI agent with a name and instructions.
+- Registering durable agents with the console app and running them interactively.
+- Conversation management (via threads) 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, you can run the sample:
+
+```bash
+cd dotnet/samples/04-hosting/DurableAgents/ConsoleApps/01_SingleAgent
+dotnet run --framework net10.0
+```
+
+The app will prompt you for input. You can interact with the Joker agent:
+
+```text
+=== Single Agent Console Sample ===
+Enter a message for the Joker agent (or 'exit' to quit):
+
+You: Tell me a joke about a pirate.
+Joker: Why don't pirates ever learn the alphabet? Because they always get stuck at "C"!
+
+You: Now explain the joke.
+Joker: The joke plays on the word "sea" (C), which pirates are famously associated with...
+
+You: exit
+```
+
+## Scriptable Usage
+
+You can also pipe input to the app for scriptable usage:
+
+```bash
+echo "Tell me a joke about a pirate." | dotnet run
+```
+
+The app will read from stdin, process the input, and write the response to stdout.
+
+## Viewing Agent State
+
+You can view the state of the agent in the Durable Task Scheduler dashboard:
+
+1. Open your browser and navigate to `http://localhost:8082`
+2. In the dashboard, you can view the state of the Joker agent, including its conversation history and current state
+
+The agent maintains conversation state across multiple interactions, and you can inspect this state in the dashboard to understand how the durable agents extension manages conversation context.
@@ -0,0 +1,30 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <AssemblyName>AgentOrchestration_Chaining</AssemblyName>
+    <RootNamespace>AgentOrchestration_Chaining</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.DurableTask.Client.AzureManaged" />
+    <PackageReference Include="Microsoft.DurableTask.Worker.AzureManaged" />
+    <PackageReference Include="Microsoft.Extensions.Hosting" />
+  </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.DurableTask" />
+    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.DurableTask\Microsoft.Agents.AI.DurableTask.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+</Project>
@@ -0,0 +1,6 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+namespace AgentOrchestration_Chaining;
+
+// Response model
+public sealed record TextResponse(string Text);
@@ -0,0 +1,151 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using AgentOrchestration_Chaining;
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.DurableTask;
+using Microsoft.DurableTask.Client;
+using Microsoft.DurableTask.Client.AzureManaged;
+using Microsoft.DurableTask.Worker;
+using Microsoft.DurableTask.Worker.AzureManaged;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+using Microsoft.Extensions.Logging;
+using OpenAI.Chat;
+using Environment = System.Environment;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Get DTS connection string from environment variable
+string dtsConnectionString = Environment.GetEnvironmentVariable("DURABLE_TASK_SCHEDULER_CONNECTION_STRING")
+    ?? "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None";
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// Single agent used by the orchestration to demonstrate sequential calls on the same session.
+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).AsAIAgent(WriterInstructions, WriterName);
+
+// Orchestrator function
+static async Task<string> RunOrchestratorAsync(TaskOrchestrationContext context)
+{
+    DurableAIAgent writer = context.GetAgent("WriterAgent");
+    AgentSession writerSession = await writer.CreateSessionAsync();
+
+    AgentResponse<TextResponse> initial = await writer.RunAsync<TextResponse>(
+        message: "Write a concise inspirational sentence about learning.",
+        session: writerSession);
+
+    AgentResponse<TextResponse> refined = await writer.RunAsync<TextResponse>(
+        message: $"Improve this further while keeping it under 25 words: {initial.Result.Text}",
+        session: writerSession);
+
+    return refined.Result.Text;
+}
+
+// Configure the console app to host the AI agent.
+IHost host = Host.CreateDefaultBuilder(args)
+    .ConfigureLogging(loggingBuilder => loggingBuilder.SetMinimumLevel(LogLevel.Warning))
+    .ConfigureServices(services =>
+    {
+        services.ConfigureDurableAgents(
+            options => options.AddAIAgent(writerAgent),
+            workerBuilder: builder =>
+            {
+                builder.UseDurableTaskScheduler(dtsConnectionString);
+                builder.AddTasks(registry => registry.AddOrchestratorFunc(nameof(RunOrchestratorAsync), RunOrchestratorAsync));
+            },
+            clientBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString));
+    })
+    .Build();
+
+await host.StartAsync();
+
+DurableTaskClient durableClient = host.Services.GetRequiredService<DurableTaskClient>();
+
+// Console colors for better UX
+Console.ForegroundColor = ConsoleColor.Cyan;
+Console.WriteLine("=== Single Agent Orchestration Chaining Sample ===");
+Console.ResetColor();
+Console.WriteLine("Starting orchestration...");
+Console.WriteLine();
+
+try
+{
+    // Start the orchestration
+    string instanceId = await durableClient.ScheduleNewOrchestrationInstanceAsync(
+        orchestratorName: nameof(RunOrchestratorAsync));
+
+    Console.ForegroundColor = ConsoleColor.Gray;
+    Console.WriteLine($"Orchestration started with instance ID: {instanceId}");
+    Console.WriteLine("Waiting for completion...");
+    Console.ResetColor();
+
+    // Wait for orchestration to complete
+    OrchestrationMetadata status = await durableClient.WaitForInstanceCompletionAsync(
+        instanceId,
+        getInputsAndOutputs: true,
+        CancellationToken.None);
+
+    Console.WriteLine();
+
+    if (status.RuntimeStatus == OrchestrationRuntimeStatus.Completed)
+    {
+        Console.ForegroundColor = ConsoleColor.Green;
+        Console.WriteLine("✓ Orchestration completed successfully!");
+        Console.ResetColor();
+        Console.WriteLine();
+        Console.ForegroundColor = ConsoleColor.Yellow;
+        Console.Write("Result: ");
+        Console.ResetColor();
+        Console.WriteLine(status.ReadOutputAs<string>());
+    }
+    else if (status.RuntimeStatus == OrchestrationRuntimeStatus.Failed)
+    {
+        Console.ForegroundColor = ConsoleColor.Red;
+        Console.WriteLine("✗ Orchestration failed!");
+        Console.ResetColor();
+        if (status.FailureDetails != null)
+        {
+            Console.WriteLine($"Error: {status.FailureDetails.ErrorMessage}");
+        }
+        Environment.Exit(1);
+    }
+    else
+    {
+        Console.ForegroundColor = ConsoleColor.Yellow;
+        Console.WriteLine($"Orchestration status: {status.RuntimeStatus}");
+        Console.ResetColor();
+    }
+}
+catch (Exception ex)
+{
+    Console.ForegroundColor = ConsoleColor.Red;
+    Console.Error.WriteLine($"Error: {ex.Message}");
+    Console.ResetColor();
+    Environment.Exit(1);
+}
+finally
+{
+    await host.StopAsync();
+}
@@ -0,0 +1,53 @@
+# Single Agent Orchestration Sample
+
+This sample demonstrates how to use the durable agents extension to create a simple console app that orchestrates sequential calls to a single AI agent using the same session for context continuity.
+
+## Key Concepts Demonstrated
+
+- Orchestrating multiple interactions with the same agent in a deterministic order
+- Using the same `AgentSession` across multiple calls to maintain conversational context
+- Durable orchestration with automatic checkpointing and resumption from failures
+- Waiting for orchestration completion using `WaitForInstanceCompletionAsync`
+
+## 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, you can run the sample:
+
+```bash
+cd dotnet/samples/04-hosting/DurableAgents/ConsoleApps/02_AgentOrchestration_Chaining
+dotnet run --framework net10.0
+```
+
+The app will start the orchestration, wait for it to complete, and display the result:
+
+```text
+=== Single Agent Orchestration Chaining Sample ===
+Starting orchestration...
+
+Orchestration started with instance ID: 86313f1d45fb42eeb50b1852626bf3ff
+Waiting for completion...
+
+✓ Orchestration completed successfully!
+
+Result: Learning serves as the key, opening doors to boundless opportunities and a brighter future.
+```
+
+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
+
+## Viewing Orchestration State
+
+You can view the state of the orchestration in the Durable Task Scheduler dashboard:
+
+1. Open your browser and navigate to `http://localhost:8082`
+2. In the dashboard, you can see:
+   - **Orchestrations**: View the orchestration instance, including its runtime status, input, output, and execution history
+   - **Agents**: View the state of the WriterAgent, including conversation history maintained across the orchestration steps
+
+The orchestration instance ID is displayed in the console output. You can use this ID to find the specific orchestration in the dashboard and inspect its execution details, including the sequence of agent calls and their results.
@@ -0,0 +1,30 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <AssemblyName>AgentOrchestration_Concurrency</AssemblyName>
+    <RootNamespace>AgentOrchestration_Concurrency</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.DurableTask.Client.AzureManaged" />
+    <PackageReference Include="Microsoft.DurableTask.Worker.AzureManaged" />
+    <PackageReference Include="Microsoft.Extensions.Hosting" />
+  </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.DurableTask" />
+    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.DurableTask\Microsoft.Agents.AI.DurableTask.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+</Project>
@@ -0,0 +1,6 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+namespace AgentOrchestration_Concurrency;
+
+// Response model
+public sealed record TextResponse(string Text);
@@ -0,0 +1,194 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Text.Json;
+using AgentOrchestration_Concurrency;
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.DurableTask;
+using Microsoft.DurableTask.Client;
+using Microsoft.DurableTask.Client.AzureManaged;
+using Microsoft.DurableTask.Worker;
+using Microsoft.DurableTask.Worker.AzureManaged;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+using Microsoft.Extensions.Logging;
+using OpenAI.Chat;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Get DTS connection string from environment variable
+string dtsConnectionString = Environment.GetEnvironmentVariable("DURABLE_TASK_SCHEDULER_CONNECTION_STRING")
+    ?? "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None";
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// 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 a middle school chemistry teacher. You answer questions so that middle school students can understand.";
+
+AIAgent physicistAgent = client.GetChatClient(deploymentName).AsAIAgent(PhysicistInstructions, PhysicistName);
+AIAgent chemistAgent = client.GetChatClient(deploymentName).AsAIAgent(ChemistInstructions, ChemistName);
+
+// Orchestrator function
+static async Task<object> RunOrchestratorAsync(TaskOrchestrationContext context, string prompt)
+{
+    // Get both agents
+    DurableAIAgent physicist = context.GetAgent(PhysicistName);
+    DurableAIAgent chemist = context.GetAgent(ChemistName);
+
+    // Start both agent runs concurrently
+    Task<AgentResponse<TextResponse>> physicistTask = physicist.RunAsync<TextResponse>(prompt);
+    Task<AgentResponse<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,
+    };
+}
+
+// Configure the console app to host the AI agents.
+IHost host = Host.CreateDefaultBuilder(args)
+    .ConfigureLogging(loggingBuilder => loggingBuilder.SetMinimumLevel(LogLevel.Warning))
+    .ConfigureServices(services =>
+    {
+        services.ConfigureDurableAgents(
+            options =>
+            {
+                options
+                    .AddAIAgent(physicistAgent)
+                    .AddAIAgent(chemistAgent);
+            },
+            workerBuilder: builder =>
+            {
+                builder.UseDurableTaskScheduler(dtsConnectionString);
+                builder.AddTasks(
+                    registry => registry.AddOrchestratorFunc<string, object>(nameof(RunOrchestratorAsync), RunOrchestratorAsync));
+            },
+            clientBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString));
+    })
+    .Build();
+
+await host.StartAsync();
+
+DurableTaskClient durableTaskClient = host.Services.GetRequiredService<DurableTaskClient>();
+
+// Console colors for better UX
+Console.ForegroundColor = ConsoleColor.Cyan;
+Console.WriteLine("=== Multi-Agent Concurrent Orchestration Sample ===");
+Console.ResetColor();
+Console.WriteLine("Enter a question for the agents:");
+Console.WriteLine();
+
+// Read prompt from stdin
+string? prompt = Console.ReadLine();
+if (string.IsNullOrWhiteSpace(prompt))
+{
+    Console.ForegroundColor = ConsoleColor.Red;
+    Console.Error.WriteLine("Error: Prompt is required.");
+    Console.ResetColor();
+    Environment.Exit(1);
+    return;
+}
+
+Console.WriteLine();
+Console.ForegroundColor = ConsoleColor.Gray;
+Console.WriteLine("Starting orchestration...");
+Console.ResetColor();
+
+try
+{
+    // Start the orchestration
+    string instanceId = await durableTaskClient.ScheduleNewOrchestrationInstanceAsync(
+        orchestratorName: nameof(RunOrchestratorAsync),
+        input: prompt);
+
+    Console.ForegroundColor = ConsoleColor.Gray;
+    Console.WriteLine($"Orchestration started with instance ID: {instanceId}");
+    Console.WriteLine("Waiting for completion...");
+    Console.ResetColor();
+
+    // Wait for orchestration to complete
+    OrchestrationMetadata status = await durableTaskClient.WaitForInstanceCompletionAsync(
+        instanceId,
+        getInputsAndOutputs: true,
+        CancellationToken.None);
+
+    Console.WriteLine();
+
+    if (status.RuntimeStatus == OrchestrationRuntimeStatus.Completed)
+    {
+        Console.ForegroundColor = ConsoleColor.Green;
+        Console.WriteLine("✓ Orchestration completed successfully!");
+        Console.ResetColor();
+        Console.WriteLine();
+
+        // Parse the output
+        using JsonDocument doc = JsonDocument.Parse(status.SerializedOutput!);
+        JsonElement output = doc.RootElement;
+
+        Console.ForegroundColor = ConsoleColor.Yellow;
+        Console.WriteLine("Physicist's response:");
+        Console.ResetColor();
+        Console.WriteLine(output.GetProperty("physicist").GetString());
+        Console.WriteLine();
+
+        Console.ForegroundColor = ConsoleColor.Yellow;
+        Console.WriteLine("Chemist's response:");
+        Console.ResetColor();
+        Console.WriteLine(output.GetProperty("chemist").GetString());
+    }
+    else if (status.RuntimeStatus == OrchestrationRuntimeStatus.Failed)
+    {
+        Console.ForegroundColor = ConsoleColor.Red;
+        Console.WriteLine("✗ Orchestration failed!");
+        Console.ResetColor();
+        if (status.FailureDetails != null)
+        {
+            Console.WriteLine($"Error: {status.FailureDetails.ErrorMessage}");
+        }
+        Environment.Exit(1);
+    }
+    else
+    {
+        Console.ForegroundColor = ConsoleColor.Yellow;
+        Console.WriteLine($"Orchestration status: {status.RuntimeStatus}");
+        Console.ResetColor();
+    }
+}
+catch (Exception ex)
+{
+    Console.ForegroundColor = ConsoleColor.Red;
+    Console.Error.WriteLine($"Error: {ex.Message}");
+    Console.ResetColor();
+    Environment.Exit(1);
+}
+finally
+{
+    await host.StopAsync();
+}
@@ -0,0 +1,68 @@
+# Multi-Agent Concurrent Orchestration Sample
+
+This sample demonstrates how to use the durable agents extension to create a console app that orchestrates concurrent execution of multiple AI agents using durable orchestration.
+
+## Key Concepts Demonstrated
+
+- Running multiple agents concurrently in a single orchestration
+- Using `Task.WhenAll` to wait for concurrent agent executions
+- Combining results from multiple agents into a single response
+- Waiting for orchestration completion using `WaitForInstanceCompletionAsync`
+
+## 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, you can run the sample:
+
+```bash
+cd dotnet/samples/04-hosting/DurableAgents/ConsoleApps/03_AgentOrchestration_Concurrency
+dotnet run --framework net10.0
+```
+
+The app will prompt you for a question:
+
+```text
+=== Multi-Agent Concurrent Orchestration Sample ===
+Enter a question for the agents:
+
+What is temperature?
+```
+
+The orchestration will run both agents concurrently and display their responses:
+
+```text
+Orchestration started with instance ID: 86313f1d45fb42eeb50b1852626bf3ff
+Waiting for completion...
+
+✓ Orchestration completed successfully!
+
+Physicist's response:
+Temperature is a measure of the average kinetic energy of particles in a system...
+
+Chemist's response:
+From a chemistry perspective, temperature is crucial for chemical reactions...
+```
+
+Both agents run in parallel, and the orchestration waits for both to complete before returning the combined results.
+
+## Viewing Orchestration State
+
+You can view the state of the orchestration in the Durable Task Scheduler dashboard:
+
+1. Open your browser and navigate to `http://localhost:8082`
+2. In the dashboard, you can see:
+   - **Orchestrations**: View the orchestration instance, including its runtime status, input, output, and execution history
+   - **Agents**: View the state of both the PhysicistAgent and ChemistAgent, including their individual conversation histories
+
+The orchestration instance ID is displayed in the console output. You can use this ID to find the specific orchestration in the dashboard and inspect how the concurrent agent executions were coordinated, including the timing of when each agent started and completed.
+
+## Scriptable Usage
+
+You can also pipe input to the app:
+
+```bash
+echo "What is temperature?" | dotnet run
+```
@@ -0,0 +1,30 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <AssemblyName>AgentOrchestration_Conditionals</AssemblyName>
+    <RootNamespace>AgentOrchestration_Conditionals</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.DurableTask.Client.AzureManaged" />
+    <PackageReference Include="Microsoft.DurableTask.Worker.AzureManaged" />
+    <PackageReference Include="Microsoft.Extensions.Hosting" />
+  </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.DurableTask" />
+    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.DurableTask\Microsoft.Agents.AI.DurableTask.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+</Project>
@@ -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 +1,231 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using AgentOrchestration_Conditionals;
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.DurableTask;
+using Microsoft.DurableTask.Client;
+using Microsoft.DurableTask.Client.AzureManaged;
+using Microsoft.DurableTask.Worker;
+using Microsoft.DurableTask.Worker.AzureManaged;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+using Microsoft.Extensions.Logging;
+using OpenAI.Chat;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Get DTS connection string from environment variable
+string dtsConnectionString = Environment.GetEnvironmentVariable("DURABLE_TASK_SCHEDULER_CONNECTION_STRING")
+    ?? "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None";
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// Spam detection agent
+const string SpamDetectionAgentName = "SpamDetectionAgent";
+const string SpamDetectionAgentInstructions =
+    """
+    You are an expert email spam detection system. Analyze emails and determine if they are spam.
+    Return your analysis as JSON with 'is_spam' (boolean) and 'reason' (string) fields.
+    """;
+
+// Email assistant agent
+const string EmailAssistantAgentName = "EmailAssistantAgent";
+const string EmailAssistantAgentInstructions =
+    """
+    You are a professional email assistant. Draft professional, courteous, and helpful email responses.
+    Return your response as JSON with a 'response' field containing the reply.
+    """;
+
+AIAgent spamDetectionAgent = client.GetChatClient(deploymentName).AsAIAgent(SpamDetectionAgentInstructions, SpamDetectionAgentName);
+AIAgent emailAssistantAgent = client.GetChatClient(deploymentName).AsAIAgent(EmailAssistantAgentInstructions, EmailAssistantAgentName);
+
+// Orchestrator function
+static async Task<string> RunOrchestratorAsync(TaskOrchestrationContext context, Email email)
+{
+    // Get the spam detection agent
+    DurableAIAgent spamDetectionAgent = context.GetAgent(SpamDetectionAgentName);
+    AgentSession spamSession = await spamDetectionAgent.CreateSessionAsync();
+
+    // Step 1: Check if the email is spam
+    AgentResponse<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}
+            """,
+        session: spamSession);
+    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(EmailAssistantAgentName);
+    AgentSession emailSession = await emailAssistantAgent.CreateSessionAsync();
+
+    AgentResponse<EmailResponse> emailAssistantResponse = await emailAssistantAgent.RunAsync<EmailResponse>(
+        message:
+            $"""
+            Draft a professional response to this email. Return a JSON response with a 'response' field containing the reply:
+            
+            Email ID: {email.EmailId}
+            Content: {email.EmailContent}
+            """,
+        session: emailSession);
+
+    EmailResponse emailResponse = emailAssistantResponse.Result;
+
+    return await context.CallActivityAsync<string>(nameof(SendEmail), emailResponse.Response);
+}
+
+// Activity functions
+static void HandleSpamEmail(TaskActivityContext context, string reason)
+{
+    Console.WriteLine($"Email marked as spam: {reason}");
+}
+
+static void SendEmail(TaskActivityContext context, string message)
+{
+    Console.WriteLine($"Email sent: {message}");
+}
+
+// Configure the console app to host the AI agents.
+IHost host = Host.CreateDefaultBuilder(args)
+    .ConfigureLogging(loggingBuilder => loggingBuilder.SetMinimumLevel(LogLevel.Warning))
+    .ConfigureServices(services =>
+    {
+        services.ConfigureDurableAgents(
+            options =>
+            {
+                options
+                    .AddAIAgent(spamDetectionAgent)
+                    .AddAIAgent(emailAssistantAgent);
+            },
+            workerBuilder: builder =>
+            {
+                builder.UseDurableTaskScheduler(dtsConnectionString);
+                builder.AddTasks(registry =>
+                {
+                    registry.AddOrchestratorFunc<Email>(nameof(RunOrchestratorAsync), RunOrchestratorAsync);
+                    registry.AddActivityFunc<string>(nameof(HandleSpamEmail), HandleSpamEmail);
+                    registry.AddActivityFunc<string>(nameof(SendEmail), SendEmail);
+                });
+            },
+            clientBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString));
+    })
+    .Build();
+
+await host.StartAsync();
+
+DurableTaskClient durableTaskClient = host.Services.GetRequiredService<DurableTaskClient>();
+
+// Console colors for better UX
+Console.ForegroundColor = ConsoleColor.Cyan;
+Console.WriteLine("=== Multi-Agent Conditional Orchestration Sample ===");
+Console.ResetColor();
+Console.WriteLine("Enter email content:");
+Console.WriteLine();
+
+// Read email content from stdin
+string? emailContent = Console.ReadLine();
+if (string.IsNullOrWhiteSpace(emailContent))
+{
+    Console.ForegroundColor = ConsoleColor.Red;
+    Console.Error.WriteLine("Error: Email content is required.");
+    Console.ResetColor();
+    Environment.Exit(1);
+    return;
+}
+
+// Generate email ID automatically
+Email email = new()
+{
+    EmailId = $"email-{Guid.NewGuid():N}",
+    EmailContent = emailContent
+};
+
+Console.WriteLine();
+Console.ForegroundColor = ConsoleColor.Gray;
+Console.WriteLine("Starting orchestration...");
+Console.ResetColor();
+
+try
+{
+    // Start the orchestration
+    string instanceId = await durableTaskClient.ScheduleNewOrchestrationInstanceAsync(
+        orchestratorName: nameof(RunOrchestratorAsync),
+        input: email);
+
+    Console.ForegroundColor = ConsoleColor.Gray;
+    Console.WriteLine($"Orchestration started with instance ID: {instanceId}");
+    Console.WriteLine("Waiting for completion...");
+    Console.ResetColor();
+
+    // Wait for orchestration to complete
+    OrchestrationMetadata status = await durableTaskClient.WaitForInstanceCompletionAsync(
+        instanceId,
+        getInputsAndOutputs: true,
+        CancellationToken.None);
+
+    Console.WriteLine();
+
+    if (status.RuntimeStatus == OrchestrationRuntimeStatus.Completed)
+    {
+        Console.ForegroundColor = ConsoleColor.Green;
+        Console.WriteLine("✓ Orchestration completed successfully!");
+        Console.ResetColor();
+        Console.WriteLine();
+        Console.ForegroundColor = ConsoleColor.Yellow;
+        Console.Write("Result: ");
+        Console.ResetColor();
+        Console.WriteLine(status.ReadOutputAs<string>());
+    }
+    else if (status.RuntimeStatus == OrchestrationRuntimeStatus.Failed)
+    {
+        Console.ForegroundColor = ConsoleColor.Red;
+        Console.WriteLine("✗ Orchestration failed!");
+        Console.ResetColor();
+        if (status.FailureDetails != null)
+        {
+            Console.WriteLine($"Error: {status.FailureDetails.ErrorMessage}");
+        }
+        Environment.Exit(1);
+    }
+    else
+    {
+        Console.ForegroundColor = ConsoleColor.Yellow;
+        Console.WriteLine($"Orchestration status: {status.RuntimeStatus}");
+        Console.ResetColor();
+    }
+}
+catch (Exception ex)
+{
+    Console.ForegroundColor = ConsoleColor.Red;
+    Console.Error.WriteLine($"Error: {ex.Message}");
+    Console.ResetColor();
+    Environment.Exit(1);
+}
+finally
+{
+    await host.StopAsync();
+}
@@ -0,0 +1,95 @@
+# Multi-Agent Conditional Orchestration Sample
+
+This sample demonstrates how to use the durable agents extension to create a console app that orchestrates multiple AI agents with conditional logic based on the results of previous agent interactions.
+
+## Key Concepts Demonstrated
+
+- Multi-agent orchestration with conditional branching
+- Using agent responses to determine workflow paths
+- Activity functions for non-agent operations
+- Waiting for orchestration completion using `WaitForInstanceCompletionAsync`
+
+## 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, you can run the sample:
+
+```bash
+cd dotnet/samples/04-hosting/DurableAgents/ConsoleApps/04_AgentOrchestration_Conditionals
+dotnet run --framework net10.0
+```
+
+The app will prompt you for email content. You can test both legitimate emails and spam emails:
+
+### Testing with a Legitimate Email
+
+```text
+=== Multi-Agent Conditional Orchestration Sample ===
+Enter 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!
+```
+
+The orchestration will analyze the email and display the result:
+
+```text
+Orchestration started with instance ID: 86313f1d45fb42eeb50b1852626bf3ff
+Waiting for completion...
+
+✓ Orchestration completed successfully!
+
+Result: Email sent: Thank you for your email. I'll prepare the updated figures...
+```
+
+### Testing with a Spam Email
+
+```text
+=== Multi-Agent Conditional Orchestration Sample ===
+Enter email content:
+
+URGENT! You've won $1,000,000! Click here now to claim your prize! Limited time offer! Don't miss out!
+```
+
+The orchestration will detect it as spam and display:
+
+```text
+Orchestration started with instance ID: 86313f1d45fb42eeb50b1852626bf3ff
+Waiting for completion...
+
+✓ Orchestration completed successfully!
+
+Result: Email marked as spam: Contains suspicious claims about winning money and urgent action requests...
+```
+
+## Scriptable Usage
+
+You can also pipe email content to the app:
+
+```bash
+# Test with a legitimate email
+echo "Hi John, I hope you're doing well..." | dotnet run
+
+# Test with a spam email
+echo "URGENT! You've won $1,000,000! Click here now!" | dotnet run
+```
+
+The orchestration will proceed as follows:
+
+1. The SpamDetectionAgent analyzes the email to determine if it's spam
+2. Based on the result:
+   - If spam: The orchestration calls the `HandleSpamEmail` activity function
+   - If not spam: The EmailAssistantAgent drafts a response, then the `SendEmail` activity function is called
+
+## Viewing Orchestration State
+
+You can view the state of the orchestration in the Durable Task Scheduler dashboard:
+
+1. Open your browser and navigate to `http://localhost:8082`
+2. In the dashboard, you can see:
+   - **Orchestrations**: View the orchestration instance, including its runtime status, input, output, and execution history
+   - **Agents**: View the state of both the SpamDetectionAgent and EmailAssistantAgent
+
+The orchestration instance ID is displayed in the console output. You can use this ID to find the specific orchestration in the dashboard and inspect the conditional branching logic, including which path was taken based on the spam detection result.
@@ -0,0 +1,30 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <AssemblyName>AgentOrchestration_HITL</AssemblyName>
+    <RootNamespace>AgentOrchestration_HITL</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.DurableTask.Client.AzureManaged" />
+    <PackageReference Include="Microsoft.DurableTask.Worker.AzureManaged" />
+    <PackageReference Include="Microsoft.Extensions.Hosting" />
+  </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.DurableTask" />
+    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.DurableTask\Microsoft.Agents.AI.DurableTask.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+</Project>
@@ -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 +1,336 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Text.Json;
+using AgentOrchestration_HITL;
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.DurableTask;
+using Microsoft.DurableTask.Client;
+using Microsoft.DurableTask.Client.AzureManaged;
+using Microsoft.DurableTask.Worker;
+using Microsoft.DurableTask.Worker.AzureManaged;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+using Microsoft.Extensions.Logging;
+using OpenAI.Chat;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Get DTS connection string from environment variable
+string dtsConnectionString = Environment.GetEnvironmentVariable("DURABLE_TASK_SCHEDULER_CONNECTION_STRING")
+    ?? "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None";
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// 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).AsAIAgent(WriterInstructions, WriterName);
+
+// Orchestrator function
+static async Task<object> RunOrchestratorAsync(TaskOrchestrationContext context, ContentGenerationInput input)
+{
+    // Get the writer agent
+    DurableAIAgent writerAgent = context.GetAgent("WriterAgent");
+    AgentSession writerSession = await writerAgent.CreateSessionAsync();
+
+    // Set initial status
+    context.SetCustomStatus($"Starting content generation for topic: {input.Topic}");
+
+    // Step 1: Generate initial content
+    AgentResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
+        message: $"Write a short article about '{input.Topic}' in less than 300 words.",
+        session: writerSession);
+    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}
+                """,
+            session: writerSession);
+
+        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.");
+}
+
+// Activity functions
+static void NotifyUserForApproval(TaskActivityContext context, GeneratedContent content)
+{
+    // In a real implementation, this would send notifications via email, SMS, etc.
+    Console.WriteLine(
+        $"""
+        NOTIFICATION: Please review the following content for approval:
+        Title: {content.Title}
+        Content: {content.Content}
+        Use the approval endpoint to approve or reject this content.
+        """);
+}
+
+static void PublishContent(TaskActivityContext context, GeneratedContent content)
+{
+    // In a real implementation, this would publish to a CMS, website, etc.
+    Console.WriteLine(
+        $"""
+        PUBLISHING: Content has been published successfully.
+        Title: {content.Title}
+        Content: {content.Content}
+        """);
+}
+
+// Configure the console app to host the AI agent.
+IHost host = Host.CreateDefaultBuilder(args)
+    .ConfigureLogging(loggingBuilder => loggingBuilder.SetMinimumLevel(LogLevel.Warning))
+    .ConfigureServices(services =>
+    {
+        services.ConfigureDurableAgents(
+            options => options.AddAIAgent(writerAgent),
+            workerBuilder: builder =>
+            {
+                builder.UseDurableTaskScheduler(dtsConnectionString);
+                builder.AddTasks(registry =>
+                {
+                    registry.AddOrchestratorFunc<ContentGenerationInput>(nameof(RunOrchestratorAsync), RunOrchestratorAsync);
+                    registry.AddActivityFunc<GeneratedContent>(nameof(NotifyUserForApproval), NotifyUserForApproval);
+                    registry.AddActivityFunc<GeneratedContent>(nameof(PublishContent), PublishContent);
+                });
+            },
+            clientBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString));
+    })
+    .Build();
+
+await host.StartAsync();
+
+DurableTaskClient durableTaskClient = host.Services.GetRequiredService<DurableTaskClient>();
+
+// Console colors for better UX
+Console.ForegroundColor = ConsoleColor.Cyan;
+Console.WriteLine("=== Human-in-the-Loop Orchestration Sample ===");
+Console.ResetColor();
+Console.WriteLine("Enter topic for content generation:");
+Console.WriteLine();
+
+// Read topic from stdin
+string? topic = Console.ReadLine();
+if (string.IsNullOrWhiteSpace(topic))
+{
+    Console.ForegroundColor = ConsoleColor.Red;
+    Console.Error.WriteLine("Error: Topic is required.");
+    Console.ResetColor();
+    Environment.Exit(1);
+    return;
+}
+
+// Prompt for optional parameters with defaults
+Console.WriteLine();
+Console.WriteLine("Max review attempts (default: 3):");
+string? maxAttemptsInput = Console.ReadLine();
+int maxReviewAttempts = int.TryParse(maxAttemptsInput, out int maxAttempts) && maxAttempts > 0
+    ? maxAttempts
+    : 3;
+
+Console.WriteLine("Approval timeout in hours (default: 72):");
+string? timeoutInput = Console.ReadLine();
+float approvalTimeoutHours = float.TryParse(timeoutInput, out float timeout) && timeout > 0
+    ? timeout
+    : 72;
+
+ContentGenerationInput input = new()
+{
+    Topic = topic,
+    MaxReviewAttempts = maxReviewAttempts,
+    ApprovalTimeoutHours = approvalTimeoutHours
+};
+
+Console.WriteLine();
+Console.ForegroundColor = ConsoleColor.Gray;
+Console.WriteLine("Starting orchestration...");
+Console.ResetColor();
+
+try
+{
+    // Start the orchestration
+    string instanceId = await durableTaskClient.ScheduleNewOrchestrationInstanceAsync(
+        orchestratorName: nameof(RunOrchestratorAsync),
+        input: input);
+
+    Console.ForegroundColor = ConsoleColor.Gray;
+    Console.WriteLine($"Orchestration started with instance ID: {instanceId}");
+    Console.WriteLine("Waiting for human approval...");
+    Console.ResetColor();
+    Console.WriteLine();
+
+    // Monitor orchestration status and handle approval prompts
+    using CancellationTokenSource cts = new();
+    Task orchestrationTask = Task.Run(async () =>
+    {
+        while (!cts.Token.IsCancellationRequested)
+        {
+            OrchestrationMetadata? status = await durableTaskClient.GetInstanceAsync(
+                instanceId,
+                getInputsAndOutputs: true,
+                cts.Token);
+
+            if (status == null)
+            {
+                await Task.Delay(TimeSpan.FromSeconds(1), cts.Token);
+                continue;
+            }
+
+            // Check if we're waiting for approval
+            if (status.SerializedCustomStatus != null)
+            {
+                string? customStatus = status.ReadCustomStatusAs<string>();
+                if (customStatus?.StartsWith("Requesting human feedback", StringComparison.OrdinalIgnoreCase) == true)
+                {
+                    // Prompt user for approval
+                    Console.ForegroundColor = ConsoleColor.Yellow;
+                    Console.WriteLine("Content is ready for review. Check the logs above for details.");
+                    Console.Write("Approve? (y/n): ");
+                    Console.ResetColor();
+
+                    string? approvalInput = Console.ReadLine();
+                    bool approved = approvalInput?.Trim().Equals("y", StringComparison.OrdinalIgnoreCase) == true;
+
+                    Console.Write("Feedback (optional): ");
+                    string? feedback = Console.ReadLine() ?? "";
+
+                    HumanApprovalResponse approvalResponse = new()
+                    {
+                        Approved = approved,
+                        Feedback = feedback
+                    };
+
+                    await durableTaskClient.RaiseEventAsync(instanceId, "HumanApproval", approvalResponse);
+                }
+            }
+
+            if (status.RuntimeStatus is OrchestrationRuntimeStatus.Completed or OrchestrationRuntimeStatus.Failed or OrchestrationRuntimeStatus.Terminated)
+            {
+                break;
+            }
+
+            await Task.Delay(TimeSpan.FromSeconds(1), cts.Token);
+        }
+    }, cts.Token);
+
+    // Wait for orchestration to complete
+    OrchestrationMetadata finalStatus = await durableTaskClient.WaitForInstanceCompletionAsync(
+        instanceId,
+        getInputsAndOutputs: true,
+        CancellationToken.None);
+
+    cts.Cancel();
+    await orchestrationTask;
+
+    Console.WriteLine();
+
+    if (finalStatus.RuntimeStatus == OrchestrationRuntimeStatus.Completed)
+    {
+        Console.ForegroundColor = ConsoleColor.Green;
+        Console.WriteLine("✓ Orchestration completed successfully!");
+        Console.ResetColor();
+        Console.WriteLine();
+
+        JsonElement output = finalStatus.ReadOutputAs<JsonElement>();
+        if (output.TryGetProperty("content", out JsonElement contentElement))
+        {
+            Console.ForegroundColor = ConsoleColor.Yellow;
+            Console.WriteLine("Published content:");
+            Console.ResetColor();
+            Console.WriteLine(contentElement.GetString());
+        }
+    }
+    else if (finalStatus.RuntimeStatus == OrchestrationRuntimeStatus.Failed)
+    {
+        Console.ForegroundColor = ConsoleColor.Red;
+        Console.WriteLine("✗ Orchestration failed!");
+        Console.ResetColor();
+        if (finalStatus.FailureDetails != null)
+        {
+            Console.WriteLine($"Error: {finalStatus.FailureDetails.ErrorMessage}");
+        }
+        Environment.Exit(1);
+    }
+    else
+    {
+        Console.ForegroundColor = ConsoleColor.Yellow;
+        Console.WriteLine($"Orchestration status: {finalStatus.RuntimeStatus}");
+        Console.ResetColor();
+    }
+}
+catch (Exception ex)
+{
+    Console.ForegroundColor = ConsoleColor.Red;
+    Console.Error.WriteLine($"Error: {ex.Message}");
+    Console.ResetColor();
+    Environment.Exit(1);
+}
+finally
+{
+    await host.StopAsync();
+}
@@ -0,0 +1,73 @@
+# Human-in-the-Loop Orchestration Sample
+
+This sample demonstrates how to use the durable agents extension to create a console app that implements a human-in-the-loop workflow using durable orchestration, including interactive approval prompts.
+
+## Key Concepts Demonstrated
+
+- Human-in-the-loop workflows with durable orchestration
+- External event handling for human approval/rejection
+- Timeout handling for approval requests
+- Iterative content refinement based on human feedback
+
+## 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, you can run the sample:
+
+```bash
+cd dotnet/samples/04-hosting/DurableAgents/ConsoleApps/05_AgentOrchestration_HITL
+dotnet run --framework net10.0
+```
+
+The app will prompt you for input:
+
+```text
+=== Human-in-the-Loop Orchestration Sample ===
+Enter topic for content generation:
+
+The Future of Artificial Intelligence
+
+Max review attempts (default: 3):
+3
+Approval timeout in hours (default: 72):
+72
+```
+
+The orchestration will generate content and prompt you for approval:
+
+```text
+Orchestration started with instance ID: 86313f1d45fb42eeb50b1852626bf3ff
+
+=== NOTIFICATION: Content Ready for Review ===
+Title: The Future of Artificial Intelligence
+
+Content:
+[Generated content appears here]
+
+Please review the content above and provide your approval.
+
+Content is ready for review. Check the logs above for details.
+Approve? (y/n): n
+Feedback (optional): Please add more details about the ethical implications.
+```
+
+The orchestration will incorporate your feedback and regenerate the content. Once approved, it will publish and complete.
+
+## Viewing Orchestration State
+
+You can view the state of the orchestration in the Durable Task Scheduler dashboard:
+
+1. Open your browser and navigate to `http://localhost:8082`
+2. In the dashboard, you can see:
+   - **Orchestrations**: View the orchestration instance, including its runtime status, custom status (which shows approval state), input, output, and execution history
+   - **Agents**: View the state of the WriterAgent, including conversation history
+
+The orchestration instance ID is displayed in the console output. You can use this ID to find the specific orchestration in the dashboard and inspect:
+
+- The custom status field, which shows the current state of the approval workflow
+- When the orchestration is waiting for external events
+- The iteration count and feedback history
+- The final published content
@@ -0,0 +1,30 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <AssemblyName>LongRunningTools</AssemblyName>
+    <RootNamespace>LongRunningTools</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.DurableTask.Client.AzureManaged" />
+    <PackageReference Include="Microsoft.DurableTask.Worker.AzureManaged" />
+    <PackageReference Include="Microsoft.Extensions.Hosting" />
+  </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.DurableTask" />
+    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.DurableTask\Microsoft.Agents.AI.DurableTask.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+</Project>
@@ -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 feedback response.
+/// </summary>
+public sealed class HumanFeedbackResponse
+{
+    [JsonPropertyName("approved")]
+    public bool Approved { get; set; }
+
+    [JsonPropertyName("feedback")]
+    public string Feedback { get; set; } = string.Empty;
+}
@@ -0,0 +1,355 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.ComponentModel;
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using LongRunningTools;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.DurableTask;
+using Microsoft.DurableTask.Client;
+using Microsoft.DurableTask.Client.AzureManaged;
+using Microsoft.DurableTask.Worker;
+using Microsoft.DurableTask.Worker.AzureManaged;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+using Microsoft.Extensions.Logging;
+using OpenAI.Chat;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Get DTS connection string from environment variable
+string dtsConnectionString = Environment.GetEnvironmentVariable("DURABLE_TASK_SCHEDULER_CONNECTION_STRING")
+    ?? "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None";
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// 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).AsAIAgent(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.
+    """;
+
+const string HumanFeedbackEventName = "HumanFeedback";
+
+// Orchestrator function
+static async Task<object> RunOrchestratorAsync(TaskOrchestrationContext context, ContentGenerationInput input)
+{
+    // Get the writer agent
+    DurableAIAgent writerAgent = context.GetAgent(WriterAgentName);
+    AgentSession writerSession = await writerAgent.CreateSessionAsync();
+
+    // Set initial status
+    context.SetCustomStatus($"Starting content generation for topic: {input.Topic}");
+
+    // Step 1: Generate initial content
+    AgentResponse<GeneratedContent> writerResponse = await writerAgent.RunAsync<GeneratedContent>(
+        message: $"Write a short article about '{input.Topic}'.",
+        session: writerSession);
+    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)
+    {
+        // NOTE: CustomStatus has a 16 KB UTF-16 limit in Durable Functions.
+        // Only include short metadata here - the full content is passed via activity inputs/outputs.
+        context.SetCustomStatus(
+            new
+            {
+                message = "Requesting human feedback.",
+                approvalTimeoutHours = input.ApprovalTimeoutHours,
+                iterationCount,
+                contentTitle = content.Title,
+            });
+
+        // Step 2: Notify user to review the content
+        await context.CallActivityAsync(nameof(NotifyUserForApproval), content);
+
+        // Step 3: Wait for human feedback with configurable timeout
+        HumanFeedbackResponse humanResponse;
+        try
+        {
+            humanResponse = await context.WaitForExternalEvent<HumanFeedbackResponse>(
+                eventName: HumanFeedbackEventName,
+                timeout: TimeSpan.FromHours(input.ApprovalTimeoutHours));
+        }
+        catch (OperationCanceledException)
+        {
+            // Timeout occurred - treat as rejection
+            context.SetCustomStatus(
+                new
+                {
+                    message = $"Human approval timed out after {input.ApprovalTimeoutHours} hour(s). Treating as rejection.",
+                    iterationCount,
+                });
+            throw new TimeoutException($"Human approval timed out after {input.ApprovalTimeoutHours} hour(s).");
+        }
+
+        if (humanResponse.Approved)
+        {
+            context.SetCustomStatus(new
+            {
+                message = "Content approved by human reviewer. Publishing content...",
+                contentTitle = content.Title,
+            });
+
+            // 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,
+                contentTitle = content.Title,
+            });
+            return new { content = content.Content };
+        }
+
+        context.SetCustomStatus(new
+        {
+            message = "Content rejected by human reviewer. Incorporating feedback and regenerating...",
+            humanFeedback = humanResponse,
+            contentTitle = content.Title,
+        });
+
+        // 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}
+                """,
+            session: writerSession);
+
+        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.");
+}
+
+// Activity functions
+static void NotifyUserForApproval(TaskActivityContext context, GeneratedContent content)
+{
+    // In a real implementation, this would send notifications via email, SMS, etc.
+    Console.ForegroundColor = ConsoleColor.DarkMagenta;
+    Console.WriteLine(
+        $"""
+        NOTIFICATION: Please review the following content for approval:
+        Title: {content.Title}
+        Content: {content.Content}
+        """);
+    Console.ResetColor();
+}
+
+static void PublishContent(TaskActivityContext context, GeneratedContent content)
+{
+    // In a real implementation, this would publish to a CMS, website, etc.
+    Console.ForegroundColor = ConsoleColor.DarkMagenta;
+    Console.WriteLine(
+        $"""
+        PUBLISHING: Content has been published successfully.
+        Title: {content.Title}
+        Content: {content.Content}
+        """);
+    Console.ResetColor();
+}
+
+// Tools that demonstrate starting orchestrations from agent tool calls.
+[Description("Starts a content generation workflow and returns the instance ID for tracking.")]
+static string StartContentGenerationWorkflow([Description("The topic for content generation")] string 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(RunOrchestratorAsync),
+        input: new ContentGenerationInput
+        {
+            Topic = topic,
+            MaxReviewAttempts = MaxReviewAttempts,
+            ApprovalTimeoutHours = ApprovalTimeoutHours
+        });
+
+    return $"Workflow started with instance ID: {instanceId}";
+}
+
+[Description("Gets the status of a workflow orchestration and returns a summary of the workflow's current status.")]
+static async Task<object> GetWorkflowStatusAsync(
+    [Description("The instance ID of the workflow to check")] string instanceId,
+    [Description("Whether to include detailed information")] bool includeDetails = true)
+{
+    // Get the current agent context using the session-static property
+    OrchestrationMetadata? status = await DurableAgentContext.Current.GetOrchestrationStatusAsync(
+        instanceId,
+        includeDetails);
+
+    if (status is null)
+    {
+        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. If approved, the workflow will be published. " +
+    "If rejected, the workflow will generate new content.")]
+static async Task SubmitHumanFeedbackAsync(
+    [Description("The instance ID of the workflow to submit feedback for")] string instanceId,
+    [Description("Feedback to submit")] HumanFeedbackResponse feedback)
+{
+    await DurableAgentContext.Current.RaiseOrchestrationEventAsync(instanceId, HumanFeedbackEventName, feedback);
+}
+
+// Configure the console app to host the AI agents.
+IHost host = Host.CreateDefaultBuilder(args)
+    .ConfigureLogging(loggingBuilder => loggingBuilder.SetMinimumLevel(LogLevel.Warning))
+    .ConfigureServices(services =>
+    {
+        services.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 =>
+                {
+                    return client.GetChatClient(deploymentName).AsAIAgent(
+                        instructions: PublisherAgentInstructions,
+                        name: PublisherAgentName,
+                        services: sp,
+                        tools: [
+                            AIFunctionFactory.Create(StartContentGenerationWorkflow),
+                            AIFunctionFactory.Create(GetWorkflowStatusAsync),
+                            AIFunctionFactory.Create(SubmitHumanFeedbackAsync),
+                        ]);
+                });
+            },
+            workerBuilder: builder =>
+            {
+                builder.UseDurableTaskScheduler(dtsConnectionString);
+                builder.AddTasks(registry =>
+                {
+                    registry.AddOrchestratorFunc<ContentGenerationInput>(nameof(RunOrchestratorAsync), RunOrchestratorAsync);
+                    registry.AddActivityFunc<GeneratedContent>(nameof(NotifyUserForApproval), NotifyUserForApproval);
+                    registry.AddActivityFunc<GeneratedContent>(nameof(PublishContent), PublishContent);
+                });
+            },
+            clientBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString));
+    })
+    .Build();
+
+await host.StartAsync();
+
+// Get the agent proxy from services
+IServiceProvider services = host.Services;
+AIAgent? agentProxy = services.GetKeyedService<AIAgent>(PublisherAgentName);
+if (agentProxy == null)
+{
+    Console.ForegroundColor = ConsoleColor.Red;
+    Console.Error.WriteLine("Agent 'Publisher' not found.");
+    Console.ResetColor();
+    Environment.Exit(1);
+    return;
+}
+
+// Console colors for better UX
+Console.ForegroundColor = ConsoleColor.Cyan;
+Console.WriteLine("=== Long Running Tools Sample ===");
+Console.ResetColor();
+Console.WriteLine("Enter a topic for the Publisher agent to write about (or 'exit' to quit):");
+Console.WriteLine();
+
+// Create a session for the conversation
+AgentSession session = await agentProxy.CreateSessionAsync();
+
+using CancellationTokenSource cts = new();
+Console.CancelKeyPress += (sender, e) =>
+{
+    e.Cancel = true;
+    cts.Cancel();
+};
+
+while (!cts.Token.IsCancellationRequested)
+{
+    // Read input from stdin
+    Console.ForegroundColor = ConsoleColor.Yellow;
+    Console.Write("You: ");
+    Console.ResetColor();
+
+    string? input = Console.ReadLine();
+    if (string.IsNullOrWhiteSpace(input) || input.Equals("exit", StringComparison.OrdinalIgnoreCase))
+    {
+        break;
+    }
+
+    // Run the agent
+    Console.ForegroundColor = ConsoleColor.Green;
+    Console.Write("Publisher: ");
+    Console.ResetColor();
+
+    try
+    {
+        AgentResponse agentResponse = await agentProxy.RunAsync(
+            message: input,
+            session: session,
+            cancellationToken: cts.Token);
+
+        Console.WriteLine(agentResponse.Text);
+        Console.WriteLine();
+    }
+    catch (Exception ex)
+    {
+        Console.ForegroundColor = ConsoleColor.Red;
+        Console.Error.WriteLine($"Error: {ex.Message}");
+        Console.ResetColor();
+        Console.WriteLine();
+    }
+
+    Console.WriteLine("(Press Enter to prompt the Publisher agent again)");
+    _ = Console.ReadLine();
+}
+
+await host.StopAsync();
@@ -0,0 +1,90 @@
+# Long Running Tools Sample
+
+This sample demonstrates how to use the durable agents extension to create a console app with agents that have 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, you can run the sample:
+
+```bash
+cd dotnet/samples/04-hosting/DurableAgents/ConsoleApps/06_LongRunningTools
+dotnet run --framework net10.0
+```
+
+The app will prompt you for input. You can interact with the Publisher agent:
+
+```text
+=== Long Running Tools Sample ===
+Enter a topic for the Publisher agent to write about (or 'exit' to quit):
+
+You: Start a content generation workflow for the topic 'The Future of Artificial Intelligence'
+Publisher: 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!
+```
+
+Behind the scenes, the publisher agent will:
+
+1. Start the content generation workflow via a tool call
+2. The workflow will generate initial content using the Writer agent and wait for human approval, which will be visible in the terminal
+
+Once the workflow is waiting for human approval, you can send approval or rejection by prompting the publisher agent accordingly.
+
+> [!NOTE]
+> You must press Enter after each message to continue the conversation. The sample is set up this way because the workflow is running in the background and may write to the console asynchronously.
+
+To tell the agent to rewrite the content with feedback, you can prompt it to reject the content with feedback.
+
+```text
+You: Reject the content with feedback: The article needs more technical depth and better examples.
+Publisher: The content has been successfully rejected with the feedback: "The article needs more technical depth and better examples." The workflow will now generate new content based on this feedback.
+```
+
+Once you're satisfied with the content, you can approve it for publishing.
+
+```text
+You: Approve the content
+Publisher: The content has been successfully approved for publishing. If you need any more assistance or have further requests, feel free to let me know!
+```
+
+Once the workflow has completed, you can get the status by prompting the publisher agent to give you the status.
+
+```text
+You: Get the status of the workflow you previously started
+Publisher: The status of the workflow with instance ID **6a04276e8d824d8d941e1dc4142cc254** is as follows:
+
+- **Execution Status:** Completed
+- **Created At:** December 22, 2025, 23:08:13 UTC
+- **Last Updated At:** December 22, 2025, 23:09:59 UTC
+- **Workflow Status:** 
+  - Message: Content published successfully at December 22, 2025, 23:09:59 UTC
+  - Human Feedback: Approved
+```
+
+## Viewing Agent and Orchestration State
+
+You can view the state of both the agent and the orchestrations it starts in the Durable Task Scheduler dashboard:
+
+1. Open your browser and navigate to `http://localhost:8082`
+2. In the dashboard, you can see:
+   - **Agents**: View the state of the Publisher agent, including its conversation history and tool call history
+   - **Orchestrations**: View the content generation orchestration instances that were started by the agent via tool calls, including their runtime status, custom status, input, output, and execution history
+
+When the publisher agent starts a workflow, the orchestration instance ID is included in the agent's response. You can use this ID to find the specific orchestration in the dashboard and inspect:
+
+- The orchestration's execution progress
+- When it's waiting for human approval (visible in custom status)
+- The content generation workflow state
+- The WriterAgent state within the orchestration
+
+This demonstrates how agents can manage long-running workflows and how you can monitor both the agent's state and the workflows it orchestrates.
@@ -0,0 +1,31 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <OutputType>Exe</OutputType>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <AssemblyName>ReliableStreaming</AssemblyName>
+    <RootNamespace>ReliableStreaming</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.DurableTask.Client.AzureManaged" />
+    <PackageReference Include="Microsoft.DurableTask.Worker.AzureManaged" />
+    <PackageReference Include="Microsoft.Extensions.Hosting" />
+    <PackageReference Include="StackExchange.Redis" />
+  </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.DurableTask" />
+    <PackageReference Include="Microsoft.Agents.AI.OpenAI" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.DurableTask\Microsoft.Agents.AI.DurableTask.csproj" />
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+</Project>
@@ -0,0 +1,367 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to implement reliable streaming for durable agents using Redis Streams.
+// It reads prompts from stdin and streams agent responses to stdout in real-time.
+
+using System.ComponentModel;
+using Azure;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using Microsoft.DurableTask.Client.AzureManaged;
+using Microsoft.DurableTask.Worker.AzureManaged;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+using Microsoft.Extensions.Logging;
+using OpenAI.Chat;
+using ReliableStreaming;
+using StackExchange.Redis;
+
+// 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_NAME")
+    ?? throw new InvalidOperationException("AZURE_OPENAI_DEPLOYMENT_NAME is not set.");
+
+// Get Redis connection string from environment variable.
+string redisConnectionString = Environment.GetEnvironmentVariable("REDIS_CONNECTION_STRING")
+    ?? "localhost:6379";
+
+// Get the Redis stream TTL from environment variable (default: 10 minutes).
+int redisStreamTtlMinutes = int.Parse(Environment.GetEnvironmentVariable("REDIS_STREAM_TTL_MINUTES") ?? "10");
+
+// Get DTS connection string from environment variable
+string dtsConnectionString = Environment.GetEnvironmentVariable("DURABLE_TASK_SCHEDULER_CONNECTION_STRING")
+    ?? "Endpoint=http://localhost:8080;TaskHub=default;Authentication=None";
+
+// Use Azure Key Credential if provided, otherwise use Azure CLI Credential.
+string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient client = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// Travel Planner agent instructions - designed to produce longer responses for demonstrating streaming.
+const string TravelPlannerName = "TravelPlanner";
+const string TravelPlannerInstructions =
+    """
+    You are an expert travel planner who creates detailed, personalized travel itineraries.
+    When asked to plan a trip, you should:
+    1. Create a comprehensive day-by-day itinerary
+    2. Include specific recommendations for activities, restaurants, and attractions
+    3. Provide practical tips for each destination
+    4. Consider weather and local events when making recommendations
+    5. Include estimated times and logistics between activities
+    
+    Always use the available tools to get current weather forecasts and local events
+    for the destination to make your recommendations more relevant and timely.
+    
+    Format your response with clear headings for each day and include emoji icons
+    to make the itinerary easy to scan and visually appealing.
+    """;
+
+// Mock travel tools that return hardcoded data for demonstration purposes.
+[Description("Gets the weather forecast for a destination on a specific date. Use this to provide weather-aware recommendations in the itinerary.")]
+static string GetWeatherForecast(string destination, string date)
+{
+    Dictionary<string, (string condition, int highF, int lowF)> weatherByRegion = new(StringComparer.OrdinalIgnoreCase)
+    {
+        ["Tokyo"] = ("Partly cloudy with a chance of light rain", 58, 45),
+        ["Paris"] = ("Overcast with occasional drizzle", 52, 41),
+        ["New York"] = ("Clear and cold", 42, 28),
+        ["London"] = ("Foggy morning, clearing in afternoon", 48, 38),
+        ["Sydney"] = ("Sunny and warm", 82, 68),
+        ["Rome"] = ("Sunny with light breeze", 62, 48),
+        ["Barcelona"] = ("Partly sunny", 59, 47),
+        ["Amsterdam"] = ("Cloudy with light rain", 46, 38),
+        ["Dubai"] = ("Sunny and hot", 85, 72),
+        ["Singapore"] = ("Tropical thunderstorms in afternoon", 88, 77),
+        ["Bangkok"] = ("Hot and humid, afternoon showers", 91, 78),
+        ["Los Angeles"] = ("Sunny and pleasant", 72, 55),
+        ["San Francisco"] = ("Morning fog, afternoon sun", 62, 52),
+        ["Seattle"] = ("Rainy with breaks", 48, 40),
+        ["Miami"] = ("Warm and sunny", 78, 65),
+        ["Honolulu"] = ("Tropical paradise weather", 82, 72),
+    };
+
+    (string condition, int highF, int lowF) forecast = ("Partly cloudy", 65, 50);
+    foreach (KeyValuePair<string, (string, int, int)> entry in weatherByRegion)
+    {
+        if (destination.Contains(entry.Key, StringComparison.OrdinalIgnoreCase))
+        {
+            forecast = entry.Value;
+            break;
+        }
+    }
+
+    return $"""
+        Weather forecast for {destination} on {date}:
+        Conditions: {forecast.condition}
+        High: {forecast.highF}°F ({(forecast.highF - 32) * 5 / 9}°C)
+        Low: {forecast.lowF}°F ({(forecast.lowF - 32) * 5 / 9}°C)
+        
+        Recommendation: {GetWeatherRecommendation(forecast.condition)}
+        """;
+}
+
+[Description("Gets local events and activities happening at a destination around a specific date. Use this to suggest timely activities and experiences.")]
+static string GetLocalEvents(string destination, string date)
+{
+    Dictionary<string, string[]> eventsByCity = new(StringComparer.OrdinalIgnoreCase)
+    {
+        ["Tokyo"] = [
+            "🎭 Kabuki Theater Performance at Kabukiza Theatre - Traditional Japanese drama",
+            "🌸 Winter Illuminations at Yoyogi Park - Spectacular light displays",
+            "🍜 Ramen Festival at Tokyo Station - Sample ramen from across Japan",
+            "🎮 Gaming Expo at Tokyo Big Sight - Latest video games and technology",
+        ],
+        ["Paris"] = [
+            "🎨 Impressionist Exhibition at Musée d'Orsay - Extended evening hours",
+            "🍷 Wine Tasting Tour in Le Marais - Local sommelier guided",
+            "🎵 Jazz Night at Le Caveau de la Huchette - Historic jazz club",
+            "🥐 French Pastry Workshop - Learn from master pâtissiers",
+        ],
+        ["New York"] = [
+            "🎭 Broadway Show: Hamilton - Limited engagement performances",
+            "🏀 Knicks vs Lakers at Madison Square Garden",
+            "🎨 Modern Art Exhibit at MoMA - New installations",
+            "🍕 Pizza Walking Tour of Brooklyn - Artisan pizzerias",
+        ],
+        ["London"] = [
+            "👑 Royal Collection Exhibition at Buckingham Palace",
+            "🎭 West End Musical: The Phantom of the Opera",
+            "🍺 Craft Beer Festival at Brick Lane",
+            "🎪 Winter Wonderland at Hyde Park - Rides and markets",
+        ],
+        ["Sydney"] = [
+            "🏄 Pro Surfing Competition at Bondi Beach",
+            "🎵 Opera at Sydney Opera House - La Bohème",
+            "🦘 Wildlife Night Safari at Taronga Zoo",
+            "🍽️ Harbor Dinner Cruise with fireworks",
+        ],
+        ["Rome"] = [
+            "🏛️ After-Hours Vatican Tour - Skip the crowds",
+            "🍝 Pasta Making Class in Trastevere",
+            "🎵 Classical Concert at Borghese Gallery",
+            "🍷 Wine Tasting in Roman Cellars",
+        ],
+    };
+
+    string[] events = [
+        "🎭 Local theater performance",
+        "🍽️ Food and wine festival",
+        "🎨 Art gallery opening",
+        "🎵 Live music at local venues",
+    ];
+
+    foreach (KeyValuePair<string, string[]> entry in eventsByCity)
+    {
+        if (destination.Contains(entry.Key, StringComparison.OrdinalIgnoreCase))
+        {
+            events = entry.Value;
+            break;
+        }
+    }
+
+    string eventList = string.Join("\n• ", events);
+    return $"""
+        Local events in {destination} around {date}:
+        
+        • {eventList}
+        
+        💡 Tip: Book popular events in advance as they may sell out quickly!
+        """;
+}
+
+static string GetWeatherRecommendation(string condition)
+{
+    return condition switch
+    {
+        string c when c.Contains("rain", StringComparison.OrdinalIgnoreCase) || c.Contains("drizzle", StringComparison.OrdinalIgnoreCase) =>
+            "Bring an umbrella and waterproof jacket. Consider indoor activities for backup.",
+        string c when c.Contains("fog", StringComparison.OrdinalIgnoreCase) =>
+            "Morning visibility may be limited. Plan outdoor sightseeing for afternoon.",
+        string c when c.Contains("cold", StringComparison.OrdinalIgnoreCase) =>
+            "Layer up with warm clothing. Hot drinks and cozy cafés recommended.",
+        string c when c.Contains("hot", StringComparison.OrdinalIgnoreCase) || c.Contains("warm", StringComparison.OrdinalIgnoreCase) =>
+            "Stay hydrated and use sunscreen. Plan strenuous activities for cooler morning hours.",
+        string c when c.Contains("thunder", StringComparison.OrdinalIgnoreCase) || c.Contains("storm", StringComparison.OrdinalIgnoreCase) =>
+            "Keep an eye on weather updates. Have indoor alternatives ready.",
+        _ => "Pleasant conditions expected. Great day for outdoor exploration!"
+    };
+}
+
+// Configure the console app to host the AI agent.
+IHost host = Host.CreateDefaultBuilder(args)
+    .ConfigureLogging(loggingBuilder => loggingBuilder.SetMinimumLevel(LogLevel.Warning))
+    .ConfigureServices(services =>
+    {
+        services.ConfigureDurableAgents(
+            options =>
+            {
+                // Define the Travel Planner agent with tools for weather and events
+                options.AddAIAgentFactory(TravelPlannerName, sp =>
+                {
+                    return client.GetChatClient(deploymentName).AsAIAgent(
+                        instructions: TravelPlannerInstructions,
+                        name: TravelPlannerName,
+                        services: sp,
+                        tools: [
+                            AIFunctionFactory.Create(GetWeatherForecast),
+                            AIFunctionFactory.Create(GetLocalEvents),
+                        ]);
+                });
+            },
+            workerBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString),
+            clientBuilder: builder => builder.UseDurableTaskScheduler(dtsConnectionString));
+
+        // Register Redis connection as a singleton
+        services.AddSingleton<IConnectionMultiplexer>(_ =>
+            ConnectionMultiplexer.Connect(redisConnectionString));
+
+        // Register the Redis stream response handler - this captures agent responses
+        // and publishes them to Redis Streams for reliable delivery.
+        services.AddSingleton(sp =>
+            new RedisStreamResponseHandler(
+                sp.GetRequiredService<IConnectionMultiplexer>(),
+                TimeSpan.FromMinutes(redisStreamTtlMinutes)));
+        services.AddSingleton<IAgentResponseHandler>(sp =>
+            sp.GetRequiredService<RedisStreamResponseHandler>());
+    })
+    .Build();
+
+await host.StartAsync();
+
+// Get the agent proxy from services
+IServiceProvider services = host.Services;
+AIAgent? agentProxy = services.GetKeyedService<AIAgent>(TravelPlannerName);
+RedisStreamResponseHandler streamHandler = services.GetRequiredService<RedisStreamResponseHandler>();
+
+if (agentProxy == null)
+{
+    Console.ForegroundColor = ConsoleColor.Red;
+    Console.Error.WriteLine($"Agent '{TravelPlannerName}' not found.");
+    Console.ResetColor();
+    Environment.Exit(1);
+    return;
+}
+
+// Console colors for better UX
+Console.ForegroundColor = ConsoleColor.Cyan;
+Console.WriteLine("=== Reliable Streaming Sample ===");
+Console.ResetColor();
+Console.WriteLine("Enter a travel planning request (or 'exit' to quit):");
+Console.WriteLine();
+
+string? lastCursor = null;
+
+async Task ReadStreamTask(string conversationId, string? cursor, CancellationToken cancellationToken)
+{
+    // Initialize lastCursor to the starting cursor position
+    // This ensures we have a valid cursor even if cancellation happens before any chunks are processed
+    lastCursor = cursor;
+
+    await foreach (StreamChunk chunk in streamHandler.ReadStreamAsync(conversationId, cursor, cancellationToken))
+    {
+        if (chunk.Error != null)
+        {
+            Console.ForegroundColor = ConsoleColor.Red;
+            Console.Error.WriteLine($"\n[Error: {chunk.Error}]");
+            Console.ResetColor();
+            break;
+        }
+
+        if (chunk.IsDone)
+        {
+            Console.WriteLine();
+            Console.WriteLine();
+            break;
+        }
+
+        if (chunk.Text != null)
+        {
+            Console.Write(chunk.Text);
+            Console.Out.Flush();
+        }
+
+        // Always update lastCursor to track the latest entry ID, even if text is null
+        // This ensures we can resume from the correct position after interruption
+        if (!string.IsNullOrEmpty(chunk.EntryId))
+        {
+            lastCursor = chunk.EntryId;
+        }
+    }
+}
+
+// New conversation: prompt from stdin
+Console.ForegroundColor = ConsoleColor.Yellow;
+Console.Write("You: ");
+Console.ResetColor();
+
+string? prompt = Console.ReadLine();
+if (string.IsNullOrWhiteSpace(prompt) || prompt.Equals("exit", StringComparison.OrdinalIgnoreCase))
+{
+    return;
+}
+
+// Create a new agent session
+AgentSession session = await agentProxy.CreateSessionAsync();
+AgentSessionId sessionId = session.GetService<AgentSessionId>();
+string conversationId = sessionId.ToString();
+
+Console.ForegroundColor = ConsoleColor.Green;
+Console.WriteLine($"Conversation ID: {conversationId}");
+Console.WriteLine("Press [Enter] to interrupt the stream.");
+Console.ResetColor();
+
+// Run the agent in the background
+DurableAgentRunOptions options = new() { IsFireAndForget = true };
+await agentProxy.RunAsync(prompt, session, options, CancellationToken.None);
+
+bool streamCompleted = false;
+while (!streamCompleted)
+{
+    // On a key press, cancel the cancellation token to stop the stream
+    using CancellationTokenSource userCancellationSource = new();
+    _ = Task.Run(() =>
+    {
+        _ = Console.ReadLine();
+        userCancellationSource.Cancel();
+    });
+
+    try
+    {
+        // Start reading the stream and wait for it to complete
+        await ReadStreamTask(conversationId, lastCursor, userCancellationSource.Token);
+        streamCompleted = true;
+    }
+    catch (OperationCanceledException)
+    {
+        Console.ForegroundColor = ConsoleColor.Yellow;
+        Console.WriteLine("Stream cancelled. Press [Enter] to reconnect and resume the stream from the last cursor.");
+        // Ensure lastCursor is set - if it's still null, we at least have the starting cursor
+        string cursorValue = lastCursor ?? "(n/a)";
+        Console.WriteLine($"Last cursor: {cursorValue}");
+        Console.ResetColor();
+        // Explicitly flush to ensure the message is written immediately
+        Console.Out.Flush();
+    }
+
+    if (!streamCompleted)
+    {
+        Console.ReadLine();
+        Console.ForegroundColor = ConsoleColor.Green;
+        Console.WriteLine($"Resuming conversation: {conversationId} from cursor: {lastCursor ?? "(beginning)"}");
+        Console.ResetColor();
+    }
+}
+
+Console.ForegroundColor = ConsoleColor.Green;
+Console.WriteLine("Conversation completed.");
+Console.ResetColor();
+
+await host.StopAsync();
@@ -0,0 +1,181 @@
+# Reliable Streaming with Redis
+
+This sample demonstrates how to implement reliable streaming for durable agents using Redis Streams as a message broker. It enables clients to disconnect and reconnect to ongoing agent responses without losing messages, inspired by [OpenAI's background mode](https://platform.openai.com/docs/guides/background) for the Responses API.
+
+## Key Concepts Demonstrated
+
+- **Reliable message delivery**: Agent responses are persisted to Redis Streams, allowing clients to resume from any point
+- **Real-time streaming**: Chunks are printed to stdout as they arrive (like `tail -f`)
+- **Cursor-based resumption**: Each chunk includes an entry ID that can be used to resume the stream
+- **Fire-and-forget agent invocation**: The agent runs in the background while the client streams from Redis
+
+## 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.
+
+### Additional Requirements: Redis
+
+This sample requires a Redis instance. Start a local Redis instance using Docker:
+
+```bash
+docker run -d --name redis -p 6379:6379 redis:latest
+```
+
+To verify Redis is running:
+
+```bash
+docker ps | grep redis
+```
+
+## Running the Sample
+
+With the environment setup, you can run the sample:
+
+```bash
+cd dotnet/samples/04-hosting/DurableAgents/ConsoleApps/07_ReliableStreaming
+dotnet run --framework net10.0
+```
+
+The app will prompt you for a travel planning request:
+
+```text
+=== Reliable Streaming Sample ===
+Enter a travel planning request (or 'exit' to quit):
+
+You: Plan a 7-day trip to Tokyo, Japan for next month. Include daily activities, restaurant recommendations, and tips for getting around.
+```
+
+The agent's response will stream to your console in real-time as chunks arrive from Redis:
+
+```text
+Starting new conversation: @dafx-travelplanner@a1b2c3d4e5f67890abcdef1234567890
+Press [Enter] to interrupt the stream.
+
+TravelPlanner: # 7-Day Tokyo Adventure
+
+## Day 1: Arrival and Exploration
+...
+```
+
+### Demonstrating Stream Interruption and Resumption
+
+This is the key feature of reliable streaming. Follow these steps to see it in action:
+
+1. **Start a stream**: Run the app and enter a travel planning request
+2. **Note the conversation ID**: The conversation ID is displayed at the start of the stream (e.g., `Starting new conversation: @dafx-travelplanner@a1b2c3d4e5f67890abcdef1234567890`)
+3. **Interrupt the stream**: While the agent is still generating text, press **`Enter`** to interrupt. The agent continues running in the background - your messages are being saved to Redis.
+4. **Resume the stream**: Press **`Enter`** again to reconnect and resume the stream from the last cursor position. The app will automatically resume from where it left off.
+
+```text
+Starting new conversation: @dafx-travelplanner@a1b2c3d4e5f67890abcdef1234567890
+Press [Enter] to interrupt the stream.
+
+TravelPlanner: # 7-Day Tokyo Adventure
+
+## Day 1: Arrival and Exploration
+[Streaming content...]
+
+[Press Enter to interrupt]
+Stream cancelled. Press [Enter] to reconnect and resume the stream from the last cursor.
+Last cursor: 1734567890123-0
+
+[Press Enter to resume]
+Resuming conversation: @dafx-travelplanner@a1b2c3d4e5f67890abcdef1234567890 from cursor: 1734567890123-0
+
+[Stream continues from where it left off...]
+```
+
+## Viewing Agent State
+
+You can view the state of the agent in the Durable Task Scheduler dashboard:
+
+1. Open your browser and navigate to `http://localhost:8082`
+2. In the dashboard, you can see:
+   - **Agents**: View the state of the TravelPlanner agent, including conversation history and current state
+   - **Orchestrations**: View any orchestrations that may have been triggered by the agent
+
+The conversation ID displayed in the console output (shown as "Starting new conversation: {conversationId}") corresponds to the agent's conversation thread. You can use this to identify the agent in the dashboard and inspect:
+
+- The agent's conversation state
+- Tool calls made by the agent (weather and events lookups)
+- The streaming response state
+
+Note that while the console app streams responses from Redis, the agent state in DTS shows the underlying durable agent execution, including all tool calls and conversation context.
+
+## Architecture Overview
+
+```text
+┌─────────────┐      stdin (prompt)     ┌─────────────────────┐
+│   Client    │  ─────────────────────► │  Console App        │
+│  (stdin)    │                         │  (Program.cs)       │
+└─────────────┘                         └──────────────┬──────┘
+       ▲                                               │
+       │ stdout (chunks)                    Signal Entity
+       │                                               │
+       │                                               ▼
+       │                                    ┌─────────────────────┐
+       │                                    │   AgentEntity       │
+       │                                    │   (Durable Entity)  │
+       │                                    └──────────┬──────────┘
+       │                                               │
+       │                                    IAgentResponseHandler
+       │                                               │
+       │                                               ▼
+       │                                    ┌─────────────────────┐
+       │                                    │ RedisStreamResponse │
+       │                                    │      Handler        │
+       │                                    └──────────┬──────────┘
+       │                                               │
+       │                                     XADD (write)
+       │                                               │
+       │                                               ▼
+       │                                    ┌─────────────────────┐
+       └─────────── XREAD (poll) ────────── │   Redis Streams     │
+                                            │  (Durable Log)      │
+                                            └─────────────────────┘
+```
+
+### Data Flow
+
+1. **Client sends prompt**: The console app reads the prompt from stdin and generates a new agent thread.
+
+2. **Agent invoked**: The durable agent is signaled to run the travel planner agent. This is fire-and-forget from the console app's perspective.
+
+3. **Responses captured**: As the agent generates responses, the `RedisStreamResponseHandler` (implementing `IAgentResponseHandler`) extracts the text from each `AgentRunResponseUpdate` and publishes it to a Redis Stream keyed by the agent session's conversation ID.
+
+4. **Client polls Redis**: The console app streams events by polling the Redis Stream and printing chunks to stdout as they arrive.
+
+5. **Resumption**: If the client interrupts the stream (e.g., by pressing Enter in the sample), it can resume from the last cursor position by providing the conversation ID and cursor to the call to resume the stream.
+
+## Message Delivery Guarantees
+
+This sample provides **at-least-once delivery** with the following characteristics:
+
+- **Durability**: Messages are persisted to Redis Streams with configurable TTL (default: 10 minutes).
+- **Ordering**: Messages are delivered in order within a session.
+- **Real-time**: Chunks are printed as soon as they arrive from Redis.
+
+### Important Considerations
+
+- **No exactly-once delivery**: If a client disconnects exactly when receiving a message, it may receive that message again upon resumption. Clients should handle duplicate messages idempotently.
+- **TTL expiration**: Streams expire after the configured TTL. Clients cannot resume streams that have expired.
+- **Redis guarantees**: Redis streams are backed by Redis persistence mechanisms (RDB/AOF). Ensure your Redis instance is configured for durability as needed.
+
+## Configuration
+
+| Environment Variable | Description | Default |
+|---------------------|-------------|---------|
+| `REDIS_CONNECTION_STRING` | Redis connection string | `localhost:6379` |
+| `REDIS_STREAM_TTL_MINUTES` | How long streams are retained after last write | `10` |
+| `AZURE_OPENAI_ENDPOINT` | Azure OpenAI endpoint URL | (required) |
+| `AZURE_OPENAI_DEPLOYMENT_NAME` | Azure OpenAI deployment name | (required) |
+| `AZURE_OPENAI_API_KEY` | API key (optional, uses Azure CLI auth if not set) | (optional) |
+
+## Cleanup
+
+To stop and remove the Redis Docker containers:
+
+```bash
+docker stop redis
+docker rm redis
+```
@@ -0,0 +1,216 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Runtime.CompilerServices;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.DurableTask;
+using StackExchange.Redis;
+
+namespace ReliableStreaming;
+
+/// <summary>
+/// Represents a chunk of data read from a Redis stream.
+/// </summary>
+/// <param name="EntryId">The Redis stream entry ID (can be used as a cursor for resumption).</param>
+/// <param name="Text">The text content of the chunk, or null if this is a completion/error marker.</param>
+/// <param name="IsDone">True if this chunk marks the end of the stream.</param>
+/// <param name="Error">An error message if something went wrong, or null otherwise.</param>
+public readonly record struct StreamChunk(string EntryId, string? Text, bool IsDone, string? Error);
+
+/// <summary>
+/// An implementation of <see cref="IAgentResponseHandler"/> that publishes agent response updates
+/// to Redis Streams for reliable delivery. This enables clients to disconnect and reconnect
+/// to ongoing agent responses without losing messages.
+/// </summary>
+/// <remarks>
+/// <para>
+/// Redis Streams provide a durable, append-only log that supports consumer groups and message
+/// acknowledgment. This implementation uses auto-generated IDs (which are timestamp-based)
+/// as sequence numbers, allowing clients to resume from any point in the stream.
+/// </para>
+/// <para>
+/// Each agent session gets its own Redis Stream, keyed by session ID. The stream entries
+/// contain text chunks extracted from <see cref="AgentResponseUpdate"/> objects.
+/// </para>
+/// </remarks>
+public sealed class RedisStreamResponseHandler : IAgentResponseHandler
+{
+    private const int MaxEmptyReads = 300; // 5 minutes at 1 second intervals
+    private const int PollIntervalMs = 1000;
+
+    private readonly IConnectionMultiplexer _redis;
+    private readonly TimeSpan _streamTtl;
+
+    /// <summary>
+    /// Initializes a new instance of the <see cref="RedisStreamResponseHandler" /> class.
+    /// </summary>
+    /// <param name="redis">The Redis connection multiplexer.</param>
+    /// <param name="streamTtl">The time-to-live for stream entries. Streams will expire after this duration of inactivity.</param>
+    public RedisStreamResponseHandler(IConnectionMultiplexer redis, TimeSpan streamTtl)
+    {
+        this._redis = redis;
+        this._streamTtl = streamTtl;
+    }
+
+    /// <inheritdoc/>
+    public async ValueTask OnStreamingResponseUpdateAsync(
+        IAsyncEnumerable<AgentResponseUpdate> messageStream,
+        CancellationToken cancellationToken)
+    {
+        // Get the current session ID from the DurableAgentContext
+        // This is set by the AgentEntity before invoking the response handler
+        DurableAgentContext context = DurableAgentContext.Current
+            ?? throw new InvalidOperationException("DurableAgentContext.Current is not set. This handler must be used within a durable agent context.");
+
+        // Get conversation ID from the current session context, which is only available in the context of
+        // a durable agent execution.
+        string conversationId = context.CurrentSession.GetService<AgentSessionId>().ToString();
+        if (string.IsNullOrEmpty(conversationId))
+        {
+            throw new InvalidOperationException("Unable to determine conversation ID from the current session.");
+        }
+
+        string streamKey = GetStreamKey(conversationId);
+
+        IDatabase db = this._redis.GetDatabase();
+        int sequenceNumber = 0;
+
+        await foreach (AgentResponseUpdate update in messageStream.WithCancellation(cancellationToken))
+        {
+            // Extract just the text content - this avoids serialization round-trip issues
+            string text = update.Text;
+
+            // Only publish non-empty text chunks
+            if (!string.IsNullOrEmpty(text))
+            {
+                // Create the stream entry with the text and metadata
+                NameValueEntry[] entries =
+                [
+                    new NameValueEntry("text", text),
+                    new NameValueEntry("sequence", sequenceNumber++),
+                    new NameValueEntry("timestamp", DateTimeOffset.UtcNow.ToUnixTimeMilliseconds()),
+                ];
+
+                // Add to the Redis Stream with auto-generated ID (timestamp-based)
+                await db.StreamAddAsync(streamKey, entries);
+
+                // Refresh the TTL on each write to keep the stream alive during active streaming
+                await db.KeyExpireAsync(streamKey, this._streamTtl);
+            }
+        }
+
+        // Add a sentinel entry to mark the end of the stream
+        NameValueEntry[] endEntries =
+        [
+            new NameValueEntry("text", ""),
+            new NameValueEntry("sequence", sequenceNumber),
+            new NameValueEntry("timestamp", DateTimeOffset.UtcNow.ToUnixTimeMilliseconds()),
+            new NameValueEntry("done", "true"),
+        ];
+        await db.StreamAddAsync(streamKey, endEntries);
+
+        // Set final TTL - the stream will be cleaned up after this duration
+        await db.KeyExpireAsync(streamKey, this._streamTtl);
+    }
+
+    /// <inheritdoc/>
+    public ValueTask OnAgentResponseAsync(AgentResponse message, CancellationToken cancellationToken)
+    {
+        // This handler is optimized for streaming responses.
+        // For non-streaming responses, we don't need to store in Redis since
+        // the response is returned directly to the caller.
+        return ValueTask.CompletedTask;
+    }
+
+    /// <summary>
+    /// Reads chunks from a Redis stream for the given session, yielding them as they become available.
+    /// </summary>
+    /// <param name="conversationId">The conversation ID to read from.</param>
+    /// <param name="cursor">Optional cursor to resume from. If null, reads from the beginning.</param>
+    /// <param name="cancellationToken">Cancellation token.</param>
+    /// <returns>An async enumerable of stream chunks.</returns>
+    public async IAsyncEnumerable<StreamChunk> ReadStreamAsync(
+        string conversationId,
+        string? cursor,
+        [EnumeratorCancellation] CancellationToken cancellationToken)
+    {
+        string streamKey = GetStreamKey(conversationId);
+
+        IDatabase db = this._redis.GetDatabase();
+        string startId = string.IsNullOrEmpty(cursor) ? "0-0" : cursor;
+
+        int emptyReadCount = 0;
+        bool hasSeenData = false;
+
+        while (!cancellationToken.IsCancellationRequested)
+        {
+            StreamEntry[]? entries = null;
+            string? errorMessage = null;
+
+            try
+            {
+                entries = await db.StreamReadAsync(streamKey, startId, count: 100);
+            }
+            catch (Exception ex)
+            {
+                errorMessage = ex.Message;
+            }
+
+            if (errorMessage != null)
+            {
+                yield return new StreamChunk(startId, null, false, errorMessage);
+                yield break;
+            }
+
+            // entries is guaranteed to be non-null if errorMessage is null
+            if (entries!.Length == 0)
+            {
+                if (!hasSeenData)
+                {
+                    emptyReadCount++;
+                    if (emptyReadCount >= MaxEmptyReads)
+                    {
+                        yield return new StreamChunk(
+                            startId,
+                            null,
+                            false,
+                            $"Stream not found or timed out after {MaxEmptyReads * PollIntervalMs / 1000} seconds");
+                        yield break;
+                    }
+                }
+
+                await Task.Delay(PollIntervalMs, cancellationToken);
+                continue;
+            }
+
+            hasSeenData = true;
+
+            foreach (StreamEntry entry in entries)
+            {
+                startId = entry.Id.ToString();
+                string? text = entry["text"];
+                string? done = entry["done"];
+
+                if (done == "true")
+                {
+                    yield return new StreamChunk(startId, null, true, null);
+                    yield break;
+                }
+
+                if (!string.IsNullOrEmpty(text))
+                {
+                    yield return new StreamChunk(startId, text, false, null);
+                }
+            }
+        }
+
+        // If we exited the loop due to cancellation, throw to signal the caller
+        cancellationToken.ThrowIfCancellationRequested();
+    }
+
+    /// <summary>
+    /// Gets the Redis Stream key for a given conversation ID.
+    /// </summary>
+    /// <param name="conversationId">The conversation ID.</param>
+    /// <returns>The Redis Stream key.</returns>
+    internal static string GetStreamKey(string conversationId) => $"agent-stream:{conversationId}";
+}
@@ -0,0 +1,109 @@
+# Console App Samples
+
+This directory contains samples for console app hosting of durable agents. These samples use standard I/O (stdin/stdout) for interaction, making them both interactive and scriptable.
+
+- **[01_SingleAgent](01_SingleAgent)**: A sample that demonstrates how to host a single conversational agent in a console app and interact with it via stdin/stdout.
+- **[02_AgentOrchestration_Chaining](02_AgentOrchestration_Chaining)**: A sample that demonstrates how to host a single conversational agent in a console app and invoke it using a durable orchestration.
+- **[03_AgentOrchestration_Concurrency](03_AgentOrchestration_Concurrency)**: A sample that demonstrates how to host multiple agents in a console 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 a console 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 interactive approval prompts.
+- **[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_ReliableStreaming](07_ReliableStreaming)**: A sample that demonstrates how to implement reliable streaming for durable agents using Redis Streams, enabling clients to disconnect and reconnect without losing messages.
+
+## 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 10.0 SDK or later](https://dotnet.microsoft.com/download/dotnet)
+- [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-5.4-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
+- [Redis](https://redis.io/) (for sample 07 only) - can be run locally using Docker
+
+### 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 console 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_API_KEY` environment variable.
+
+Bash (Linux/macOS/WSL):
+
+```bash
+export AZURE_OPENAI_API_KEY="your-api-key"
+```
+
+PowerShell:
+
+```powershell
+$env:AZURE_OPENAI_API_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`.
+
+### Environment Configuration
+
+Each sample reads configuration from environment variables. You'll need to set the following environment variables:
+
+```bash
+export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
+export AZURE_OPENAI_DEPLOYMENT_NAME="your-deployment-name"
+```
+
+### Running the Console Apps
+
+Navigate to the sample directory and run the console app:
+
+```bash
+cd dotnet/samples/04-hosting/DurableAgents/ConsoleApps/01_SingleAgent
+dotnet run --framework net10.0
+```
+
+> [!NOTE]
+> The `--framework` option is required to specify the target framework for the console app because the samples are designed to support multiple target frameworks. If you are using a different target framework, you can specify it with the `--framework` option.
+
+The app will prompt you for input via stdin.
+
+### Viewing the sample output
+
+The console app output is displayed directly in the terminal where you ran `dotnet run`. Agent responses are printed to stdout with subtle color coding for better readability.
+
+You can also see the state of agents and orchestrations in the Durable Task Scheduler dashboard at `http://localhost:8082`.
@@ -0,0 +1,9 @@
+<Project>
+
+  <Import Project="../../Directory.Build.props" />
+
+  <!-- Remove the Environment alias from parent Directory.Build.props to allow System.Environment usage -->
+  <ItemGroup>
+    <Using Remove="SampleHelpers.SampleEnvironment" />
+  </ItemGroup>
+</Project>
@@ -0,0 +1,42 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <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 +1,215 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Agents.AI.Workflows;
+
+namespace SequentialWorkflow;
+
+/// <summary>
+/// Looks up an order by its ID and return an Order object.
+/// </summary>
+internal sealed class OrderLookup() : Executor<string, Order>("OrderLookup")
+{
+    public override async ValueTask<Order> HandleAsync(
+        string message,
+        IWorkflowContext context,
+        CancellationToken cancellationToken = default)
+    {
+        Console.WriteLine();
+        Console.ForegroundColor = ConsoleColor.Magenta;
+        Console.WriteLine("┌─────────────────────────────────────────────────────────────────┐");
+        Console.WriteLine($"│ [Activity] OrderLookup: Starting lookup for order '{message}'");
+        Console.ResetColor();
+
+        // Simulate database lookup with delay
+        await Task.Delay(TimeSpan.FromMicroseconds(100), cancellationToken);
+
+        Order order = new(
+            Id: message,
+            OrderDate: DateTime.UtcNow.AddDays(-1),
+            IsCancelled: false,
+            Customer: new Customer(Name: "Jerry", Email: "jerry@example.com"));
+
+        Console.ForegroundColor = ConsoleColor.Magenta;
+        Console.WriteLine($"│ [Activity] OrderLookup: Found order '{message}' for customer '{order.Customer.Name}'");
+        Console.WriteLine("└─────────────────────────────────────────────────────────────────┘");
+        Console.ResetColor();
+
+        return order;
+    }
+}
+
+/// <summary>
+/// Cancels an order.
+/// </summary>
+internal sealed class OrderCancel() : Executor<Order, Order>("OrderCancel")
+{
+    public override async ValueTask<Order> HandleAsync(
+        Order message,
+        IWorkflowContext context,
+        CancellationToken cancellationToken = default)
+    {
+        Console.WriteLine();
+        Console.ForegroundColor = ConsoleColor.Yellow;
+        Console.WriteLine("┌─────────────────────────────────────────────────────────────────┐");
+        Console.WriteLine($"│ [Activity] OrderCancel: Starting cancellation for order '{message.Id}'");
+        Console.ResetColor();
+
+        // Simulate a slow cancellation process (e.g., calling external payment system)
+        for (int i = 1; i <= 3; i++)
+        {
+            await Task.Delay(TimeSpan.FromMilliseconds(100), cancellationToken);
+            Console.ForegroundColor = ConsoleColor.DarkYellow;
+            Console.WriteLine("│ [Activity] OrderCancel: Processing...");
+            Console.ResetColor();
+        }
+
+        Order cancelledOrder = message with { IsCancelled = true };
+
+        Console.ForegroundColor = ConsoleColor.Yellow;
+        Console.WriteLine($"│ [Activity] OrderCancel: ✓ Order '{cancelledOrder.Id}' has been cancelled");
+        Console.WriteLine("└─────────────────────────────────────────────────────────────────┘");
+        Console.ResetColor();
+
+        return cancelledOrder;
+    }
+}
+
+/// <summary>
+/// Sends a cancellation confirmation email to the customer.
+/// </summary>
+internal sealed class SendEmail() : Executor<Order, string>("SendEmail")
+{
+    public override ValueTask<string> HandleAsync(
+        Order message,
+        IWorkflowContext context,
+        CancellationToken cancellationToken = default)
+    {
+        Console.WriteLine();
+        Console.ForegroundColor = ConsoleColor.Cyan;
+        Console.WriteLine("┌─────────────────────────────────────────────────────────────────┐");
+        Console.WriteLine($"│ [Activity] SendEmail: Sending email to '{message.Customer.Email}'...");
+        Console.ResetColor();
+
+        string result = $"Cancellation email sent for order {message.Id} to {message.Customer.Email}.";
+
+        Console.ForegroundColor = ConsoleColor.Cyan;
+        Console.WriteLine("│ [Activity] SendEmail: ✓ Email sent successfully!");
+        Console.WriteLine("└─────────────────────────────────────────────────────────────────┘");
+        Console.ResetColor();
+
+        return ValueTask.FromResult(result);
+    }
+}
+
+internal sealed record Order(string Id, DateTime OrderDate, bool IsCancelled, Customer Customer);
+
+internal sealed record Customer(string Name, string Email);
+
+/// <summary>
+/// Represents a batch cancellation request with multiple order IDs and a reason.
+/// This demonstrates using a complex typed object as workflow input.
+/// </summary>
+#pragma warning disable CA1812 // Instantiated via JSON deserialization at runtime
+internal sealed record BatchCancelRequest(string[] OrderIds, string Reason, bool NotifyCustomers);
+#pragma warning restore CA1812
+
+/// <summary>
+/// Represents the result of processing a batch cancellation.
+/// </summary>
+internal sealed record BatchCancelResult(int TotalOrders, int CancelledCount, string Reason);
+
+/// <summary>
+/// Generates a status report for an order.
+/// </summary>
+internal sealed class StatusReport() : Executor<Order, string>("StatusReport")
+{
+    public override ValueTask<string> HandleAsync(
+        Order message,
+        IWorkflowContext context,
+        CancellationToken cancellationToken = default)
+    {
+        Console.WriteLine();
+        Console.ForegroundColor = ConsoleColor.Green;
+        Console.WriteLine("┌─────────────────────────────────────────────────────────────────┐");
+        Console.WriteLine($"│ [Activity] StatusReport: Generating report for order '{message.Id}'");
+        Console.ResetColor();
+
+        string status = message.IsCancelled ? "Cancelled" : "Active";
+        string result = $"Order {message.Id} for {message.Customer.Name}: Status={status}, Date={message.OrderDate:yyyy-MM-dd}";
+
+        Console.ForegroundColor = ConsoleColor.Green;
+        Console.WriteLine($"│ [Activity] StatusReport: ✓ {result}");
+        Console.WriteLine("└─────────────────────────────────────────────────────────────────┘");
+        Console.ResetColor();
+
+        return ValueTask.FromResult(result);
+    }
+}
+
+/// <summary>
+/// Processes a batch cancellation request. Accepts a complex <see cref="BatchCancelRequest"/> object
+/// as input, demonstrating how workflows can receive structured JSON input.
+/// </summary>
+internal sealed class BatchCancelProcessor() : Executor<BatchCancelRequest, BatchCancelResult>("BatchCancelProcessor")
+{
+    public override async ValueTask<BatchCancelResult> HandleAsync(
+        BatchCancelRequest message,
+        IWorkflowContext context,
+        CancellationToken cancellationToken = default)
+    {
+        Console.WriteLine();
+        Console.ForegroundColor = ConsoleColor.Yellow;
+        Console.WriteLine("┌─────────────────────────────────────────────────────────────────┐");
+        Console.WriteLine($"│ [Activity] BatchCancelProcessor: Processing {message.OrderIds.Length} orders");
+        Console.WriteLine($"│ [Activity] BatchCancelProcessor: Reason: {message.Reason}");
+        Console.WriteLine($"│ [Activity] BatchCancelProcessor: Notify customers: {message.NotifyCustomers}");
+        Console.ResetColor();
+
+        // Simulate processing each order
+        int cancelledCount = 0;
+        foreach (string orderId in message.OrderIds)
+        {
+            await Task.Delay(TimeSpan.FromMilliseconds(100), cancellationToken);
+            cancelledCount++;
+            Console.ForegroundColor = ConsoleColor.DarkYellow;
+            Console.WriteLine($"│ [Activity] BatchCancelProcessor: ✓ Cancelled order '{orderId}'");
+            Console.ResetColor();
+        }
+
+        BatchCancelResult result = new(message.OrderIds.Length, cancelledCount, message.Reason);
+
+        Console.ForegroundColor = ConsoleColor.Yellow;
+        Console.WriteLine($"│ [Activity] BatchCancelProcessor: ✓ Batch complete: {cancelledCount}/{message.OrderIds.Length} cancelled");
+        Console.WriteLine("└─────────────────────────────────────────────────────────────────┘");
+        Console.ResetColor();
+
+        return result;
+    }
+}
+
+/// <summary>
+/// Generates a summary of the batch cancellation.
+/// </summary>
+internal sealed class BatchCancelSummary() : Executor<BatchCancelResult, string>("BatchCancelSummary")
+{
+    public override ValueTask<string> HandleAsync(
+        BatchCancelResult message,
+        IWorkflowContext context,
+        CancellationToken cancellationToken = default)
+    {
+        Console.WriteLine();
+        Console.ForegroundColor = ConsoleColor.Cyan;
+        Console.WriteLine("┌─────────────────────────────────────────────────────────────────┐");
+        Console.WriteLine("│ [Activity] BatchCancelSummary: Generating summary");
+        Console.ResetColor();
+
+        string result = $"Batch cancellation complete: {message.CancelledCount}/{message.TotalOrders} orders cancelled. Reason: {message.Reason}";
+
+        Console.ForegroundColor = ConsoleColor.Cyan;
+        Console.WriteLine($"│ [Activity] BatchCancelSummary: ✓ {result}");
+        Console.WriteLine("└─────────────────────────────────────────────────────────────────┘");
+        Console.ResetColor();
+
+        return ValueTask.FromResult(result);
+    }
+}
@@ -0,0 +1,52 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates three workflows that share executors.
+// The CancelOrder workflow cancels an order and notifies the customer.
+// The OrderStatus workflow looks up an order and generates a status report.
+// The BatchCancelOrders workflow accepts a complex JSON input to cancel multiple orders.
+// Both CancelOrder and OrderStatus reuse the same OrderLookup executor, demonstrating executor sharing.
+
+using Microsoft.Agents.AI.Hosting.AzureFunctions;
+using Microsoft.Agents.AI.Workflows;
+using Microsoft.Azure.Functions.Worker.Builder;
+using Microsoft.Extensions.Hosting;
+using SequentialWorkflow;
+
+// Define executors for all workflows
+OrderLookup orderLookup = new();
+OrderCancel orderCancel = new();
+SendEmail sendEmail = new();
+StatusReport statusReport = new();
+BatchCancelProcessor batchCancelProcessor = new();
+BatchCancelSummary batchCancelSummary = new();
+
+// Build the CancelOrder workflow: OrderLookup -> OrderCancel -> SendEmail
+Workflow cancelOrder = new WorkflowBuilder(orderLookup)
+    .WithName("CancelOrder")
+    .WithDescription("Cancel an order and notify the customer")
+    .AddEdge(orderLookup, orderCancel)
+    .AddEdge(orderCancel, sendEmail)
+    .Build();
+
+// Build the OrderStatus workflow: OrderLookup -> StatusReport
+// This workflow shares the OrderLookup executor with the CancelOrder workflow.
+Workflow orderStatus = new WorkflowBuilder(orderLookup)
+    .WithName("OrderStatus")
+    .WithDescription("Look up an order and generate a status report")
+    .AddEdge(orderLookup, statusReport)
+    .Build();
+
+// Build the BatchCancelOrders workflow: BatchCancelProcessor -> BatchCancelSummary
+// This workflow demonstrates using a complex JSON object as the workflow input.
+Workflow batchCancelOrders = new WorkflowBuilder(batchCancelProcessor)
+    .WithName("BatchCancelOrders")
+    .WithDescription("Cancel multiple orders in a batch using a complex JSON input")
+    .AddEdge(batchCancelProcessor, batchCancelSummary)
+    .Build();
+
+using IHost app = FunctionsApplication
+    .CreateBuilder(args)
+    .ConfigureFunctionsWebApplication()
+    .ConfigureDurableWorkflows(workflows => workflows.AddWorkflows(cancelOrder, orderStatus, batchCancelOrders))
+    .Build();
+app.Run();
@@ -0,0 +1,147 @@
+# Sequential Workflow Sample
+
+This sample demonstrates how to use the Microsoft Agent Framework to create an Azure Functions app that hosts durable workflows with sequential executor chains. It showcases two workflows that share a common executor, demonstrating executor reuse across workflows.
+
+## Key Concepts Demonstrated
+
+- Defining workflows with sequential executor chains using `WorkflowBuilder`
+- Sharing executors across multiple workflows (the `OrderLookup` executor is used by both workflows)
+- Registering workflows with the Function app using `ConfigureDurableWorkflows`
+- Durable orchestration ensuring workflows survive process restarts and failures
+- Starting workflows via HTTP requests
+- Viewing workflow execution history and status in the Durable Task Scheduler (DTS) dashboard
+
+## Workflows
+
+This sample defines two workflows:
+
+1. **CancelOrder**: `OrderLookup` → `OrderCancel` → `SendEmail` — Looks up an order, cancels it, and sends a confirmation email.
+2. **OrderStatus**: `OrderLookup` → `StatusReport` — Looks up an order and generates a status report.
+
+Both workflows share the `OrderLookup` executor, which is registered only once by the framework.
+
+## 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 HTTP requests to the workflow endpoints.
+
+You can use the `demo.http` file to trigger the workflows, or a command line tool like `curl` as shown below:
+
+### Cancel an Order
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl -X POST http://localhost:7071/api/workflows/CancelOrder/run \
+    -H "Content-Type: text/plain" \
+    -d "12345"
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/workflows/CancelOrder/run `
+    -ContentType text/plain `
+    -Body "12345"
+```
+
+The response will confirm the workflow orchestration has started:
+
+```text
+Workflow orchestration started for CancelOrder. Orchestration runId: abc123def456
+```
+
+> **Tip:** You can provide a custom run ID by appending a `runId` query parameter:
+>
+> ```bash
+> curl -X POST "http://localhost:7071/api/workflows/CancelOrder/run?runId=my-order-123" \
+>     -H "Content-Type: text/plain" \
+>     -d "12345"
+> ```
+>
+> If not provided, a unique run ID is auto-generated.
+
+### Wait for the Workflow Result
+
+By default, the HTTP endpoint returns `202 Accepted` immediately with the run ID. If you want to wait for the workflow to complete and get the result in the response, add the `x-ms-wait-for-response: true` header:
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl -X POST http://localhost:7071/api/workflows/CancelOrder/run \
+    -H "Content-Type: text/plain" \
+    -H "x-ms-wait-for-response: true" \
+    -d "12345"
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/workflows/CancelOrder/run `
+    -ContentType text/plain `
+    -Headers @{ "x-ms-wait-for-response" = "true" } `
+    -Body "12345"
+```
+
+The response will contain the workflow result as plain text (200 OK):
+
+```text
+Cancellation email sent for order 12345 to jerry@example.com.
+```
+
+To get the result as JSON, also include the `Accept: application/json` header:
+
+```bash
+curl -X POST http://localhost:7071/api/workflows/CancelOrder/run \
+    -H "Content-Type: text/plain" \
+    -H "x-ms-wait-for-response: true" \
+    -H "Accept: application/json" \
+    -d "12345"
+```
+
+```json
+{
+    "runId": "abc123def456",
+    "workflowStatus": "Completed",
+    "result": "Cancellation email sent for order 12345 to jerry@example.com."
+}
+```
+
+In the function app logs, you will see the sequential execution of each executor:
+
+```text
+│ [Activity] OrderLookup: Starting lookup for order '12345'
+│ [Activity] OrderLookup: Found order '12345' for customer 'Jerry'
+│ [Activity] OrderCancel: Starting cancellation for order '12345'
+│ [Activity] OrderCancel: ✓ Order '12345' has been cancelled
+│ [Activity] SendEmail: Sending email to 'jerry@example.com'...
+│ [Activity] SendEmail: ✓ Email sent successfully!
+```
+
+### Get Order Status
+
+```bash
+curl -X POST http://localhost:7071/api/workflows/OrderStatus/run \
+    -H "Content-Type: text/plain" \
+    -d "12345"
+```
+
+The `OrderStatus` workflow reuses the same `OrderLookup` executor and then generates a status report:
+
+```text
+│ [Activity] OrderLookup: Starting lookup for order '12345'
+│ [Activity] OrderLookup: Found order '12345' for customer 'Jerry'
+│ [Activity] StatusReport: Generating report for order '12345'
+│ [Activity] StatusReport: ✓ Order 12345 for Jerry: Status=Active, Date=2025-01-01
+```
+
+### Viewing Workflows in the DTS Dashboard
+
+After running a workflow, you can navigate to the Durable Task Scheduler (DTS) dashboard to visualize the completed orchestration, inspect inputs/outputs for each step, and view execution history.
+
+If you are using the DTS emulator, the dashboard is available at `http://localhost:8082`.
@@ -0,0 +1,48 @@
+# Default endpoint address for local testing
+@authority=http://localhost:7071
+
+### Cancel an order
+POST {{authority}}/api/workflows/CancelOrder/run
+Content-Type: text/plain
+
+12345
+
+### Cancel an order and wait for the result
+POST {{authority}}/api/workflows/CancelOrder/run
+Content-Type: text/plain
+x-ms-wait-for-response: true
+
+12345
+
+### Cancel an order and wait for the result (JSON response)
+POST {{authority}}/api/workflows/CancelOrder/run
+Content-Type: text/plain
+Accept: application/json
+x-ms-wait-for-response: true
+
+12345
+
+### Cancel an order with a custom run ID
+POST {{authority}}/api/workflows/CancelOrder/run?runId=my-custom-id-123
+Content-Type: text/plain
+
+99999
+
+### Get order status (shares OrderLookup executor with CancelOrder)
+POST {{authority}}/api/workflows/OrderStatus/run
+Content-Type: text/plain
+
+12345
+
+### Get order status and wait for the result
+POST {{authority}}/api/workflows/OrderStatus/run
+Content-Type: text/plain
+x-ms-wait-for-response: true
+
+12345
+
+### Batch cancel orders with a complex JSON input
+POST {{authority}}/api/workflows/BatchCancelOrders/run
+Content-Type: application/json
+
+{"orderIds": ["1001", "1002", "1003"], "reason": "Customer requested cancellation", "notifyCustomers": true}
@@ -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 +1,42 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <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 +1,73 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Agents.AI.Workflows;
+
+namespace WorkflowConcurrency;
+
+/// <summary>
+/// Parses and validates the incoming question before sending to AI agents.
+/// </summary>
+internal sealed class ParseQuestionExecutor() : Executor<string, string>("ParseQuestion")
+{
+    public override ValueTask<string> HandleAsync(
+        string message,
+        IWorkflowContext context,
+        CancellationToken cancellationToken = default)
+    {
+        Console.WriteLine();
+        Console.ForegroundColor = ConsoleColor.Magenta;
+        Console.WriteLine("┌─────────────────────────────────────────────────────────────────┐");
+        Console.WriteLine("│ [ParseQuestion] Preparing question for AI agents...");
+
+        string formattedQuestion = message.Trim();
+        if (!formattedQuestion.EndsWith('?'))
+        {
+            formattedQuestion += "?";
+        }
+
+        Console.WriteLine($"│ [ParseQuestion] Question: \"{formattedQuestion}\"");
+        Console.WriteLine("│ [ParseQuestion] → Sending to Physicist and Chemist in PARALLEL...");
+        Console.WriteLine("└─────────────────────────────────────────────────────────────────┘");
+        Console.ResetColor();
+
+        return ValueTask.FromResult(formattedQuestion);
+    }
+}
+
+/// <summary>
+/// Aggregates responses from all AI agents into a comprehensive answer.
+/// This is the Fan-in point where parallel results are collected.
+/// </summary>
+internal sealed class AggregatorExecutor() : Executor<string[], string>("Aggregator")
+{
+    public override ValueTask<string> HandleAsync(
+        string[] message,
+        IWorkflowContext context,
+        CancellationToken cancellationToken = default)
+    {
+        Console.WriteLine();
+        Console.ForegroundColor = ConsoleColor.Cyan;
+        Console.WriteLine("┌─────────────────────────────────────────────────────────────────┐");
+        Console.WriteLine($"│ [Aggregator] 📋 Received {message.Length} AI agent responses");
+        Console.WriteLine("│ [Aggregator] Combining into comprehensive answer...");
+        Console.WriteLine("│ [Aggregator] ✓ Aggregation complete!");
+        Console.WriteLine("└─────────────────────────────────────────────────────────────────┘");
+        Console.ResetColor();
+
+        string aggregatedResult = "═══════════════════════════════════════════════════════════════\n" +
+                                 "                    AI EXPERT PANEL RESPONSES\n" +
+                                 "═══════════════════════════════════════════════════════════════\n\n";
+
+        for (int i = 0; i < message.Length; i++)
+        {
+            string expertLabel = i == 0 ? "⚛️ PHYSICIST" : "🧪 CHEMIST";
+            aggregatedResult += $"{expertLabel}:\n{message[i]}\n\n";
+        }
+
+        aggregatedResult += "═══════════════════════════════════════════════════════════════\n" +
+                          $"Summary: Received perspectives from {message.Length} AI experts.\n" +
+                          "═══════════════════════════════════════════════════════════════";
+
+        return ValueTask.FromResult(aggregatedResult);
+    }
+}
@@ -0,0 +1,45 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+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.Agents.AI.Workflows;
+using Microsoft.Azure.Functions.Worker.Builder;
+using Microsoft.Extensions.Hosting;
+using OpenAI.Chat;
+using WorkflowConcurrency;
+
+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.");
+string? azureOpenAiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY");
+
+// Create Azure OpenAI client
+AzureOpenAIClient openAiClient = !string.IsNullOrEmpty(azureOpenAiKey)
+    ? new AzureOpenAIClient(new Uri(endpoint), new AzureKeyCredential(azureOpenAiKey))
+    : new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential());
+ChatClient chatClient = openAiClient.GetChatClient(deploymentName);
+
+// Define the 4 executors for the workflow
+ParseQuestionExecutor parseQuestion = new();
+AIAgent physicist = chatClient.AsAIAgent("You are a physics expert. Be concise (2-3 sentences).", "Physicist");
+AIAgent chemist = chatClient.AsAIAgent("You are a chemistry expert. Be concise (2-3 sentences).", "Chemist");
+AggregatorExecutor aggregator = new();
+
+// Build workflow: ParseQuestion -> [Physicist, Chemist] (parallel) -> Aggregator
+Workflow workflow = new WorkflowBuilder(parseQuestion)
+    .WithName("ExpertReview")
+    .AddFanOutEdge(parseQuestion, [physicist, chemist])
+    .AddFanInBarrierEdge([physicist, chemist], aggregator)
+    .Build();
+
+using IHost app = FunctionsApplication
+    .CreateBuilder(args)
+    .ConfigureFunctionsWebApplication()
+    .ConfigureDurableWorkflows(workflows => workflows.AddWorkflows(workflow))
+    .Build();
+app.Run();
@@ -0,0 +1,90 @@
+# Concurrent Workflow Sample
+
+This sample demonstrates how to use the Microsoft Agent Framework to create an Azure Functions app that orchestrates concurrent execution of multiple AI agents using the fan-out/fan-in pattern within a durable workflow.
+
+## Key Concepts Demonstrated
+
+- Defining workflows with fan-out/fan-in edges for parallel execution using `WorkflowBuilder`
+- Mixing custom executors with AI agents in a single workflow
+- Concurrent execution of multiple AI agents (physics and chemistry experts)
+- Response aggregation from parallel branches into a unified result
+- Durable orchestration with automatic checkpointing and resumption from failures
+- Viewing workflow execution history and status in the Durable Task Scheduler (DTS) dashboard
+
+## Workflow
+
+This sample defines a single workflow:
+
+**ExpertReview**: `ParseQuestion` → [`Physicist`, `Chemist`] (parallel) → `Aggregator`
+
+1. **ParseQuestion** — A custom executor that validates and formats the incoming question.
+2. **Physicist** and **Chemist** — AI agents that run concurrently, each providing an expert perspective.
+3. **Aggregator** — A custom executor that combines the parallel responses into a comprehensive answer.
+
+## 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.
+
+This sample requires Azure OpenAI. Set the following environment variables:
+
+- `AZURE_OPENAI_ENDPOINT` — Your Azure OpenAI endpoint URL.
+- `AZURE_OPENAI_DEPLOYMENT` — The name of your chat model deployment.
+- `AZURE_OPENAI_KEY` (optional) — Your Azure OpenAI API key. If not set, Azure CLI credentials are used.
+
+## Running the Sample
+
+With the environment setup and function app running, you can test the sample by sending an HTTP request with a science question to the workflow endpoint.
+
+You can use the `demo.http` file to trigger the workflow, or a command line tool like `curl` as shown below:
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl -X POST http://localhost:7071/api/workflows/ExpertReview/run \
+    -H "Content-Type: text/plain" \
+    -d "What is temperature?"
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/workflows/ExpertReview/run `
+    -ContentType text/plain `
+    -Body "What is temperature?"
+```
+
+The response will confirm the workflow orchestration has started:
+
+```text
+Workflow orchestration started for ExpertReview. Orchestration runId: abc123def456
+```
+
+> **Tip:** You can provide a custom run ID by appending a `runId` query parameter:
+>
+> ```bash
+> curl -X POST "http://localhost:7071/api/workflows/ExpertReview/run?runId=my-review-123" \
+>     -H "Content-Type: text/plain" \
+>     -d "What is temperature?"
+> ```
+>
+> If not provided, a unique run ID is auto-generated.
+
+In the function app logs, you will see the fan-out/fan-in execution pattern:
+
+```text
+│ [ParseQuestion] Preparing question for AI agents...
+│ [ParseQuestion] Question: "What is temperature?"
+│ [ParseQuestion] → Sending to Physicist and Chemist in PARALLEL...
+│ [Aggregator] 📋 Received 2 AI agent responses
+│ [Aggregator] Combining into comprehensive answer...
+│ [Aggregator] ✓ Aggregation complete!
+```
+
+The Physicist and Chemist AI agents execute concurrently, and the Aggregator combines their responses into a formatted expert panel result.
+
+### Viewing Workflows in the DTS Dashboard
+
+After running a workflow, you can navigate to the Durable Task Scheduler (DTS) dashboard to visualize the completed orchestration, inspect inputs/outputs for each step, and view execution history.
+
+If you are using the DTS emulator, the dashboard is available at `http://localhost:8082`.
@@ -0,0 +1,14 @@
+# Default endpoint address for local testing
+@authority=http://localhost:7071
+
+### Prompt the agent
+POST {{authority}}/api/workflows/ExpertReview/run
+Content-Type: text/plain
+
+What is temperature?
+
+### Start with a custom run ID
+POST {{authority}}/api/workflows/ExpertReview/run?runId=my-review-123
+Content-Type: text/plain
+
+What is gravity?
@@ -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 +1,43 @@
+<Project Sdk="Microsoft.NET.Sdk">
+  <PropertyGroup>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    <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>WorkflowHITLFunctions</AssemblyName>
+    <RootNamespace>WorkflowHITLFunctions</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <FrameworkReference Include="Microsoft.AspNetCore.App" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <None Include="local.settings.json" />
+  </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.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" />
+  </ItemGroup>
+  -->
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Hosting.AzureFunctions\Microsoft.Agents.AI.Hosting.AzureFunctions.csproj" />
+  </ItemGroup>
+</Project>
@@ -0,0 +1,63 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Agents.AI.Workflows;
+
+namespace WorkflowHITLFunctions;
+
+/// <summary>Expense approval request passed to the RequestPort.</summary>
+public record ApprovalRequest(string ExpenseId, decimal Amount, string EmployeeName);
+
+/// <summary>Approval response received from the RequestPort.</summary>
+public record ApprovalResponse(bool Approved, string? Comments);
+
+/// <summary>Looks up expense details and creates an approval request.</summary>
+internal sealed class CreateApprovalRequest() : Executor<string, ApprovalRequest>("RetrieveRequest")
+{
+    public override ValueTask<ApprovalRequest> HandleAsync(
+        string message,
+        IWorkflowContext context,
+        CancellationToken cancellationToken = default)
+    {
+        // In a real scenario, this would look up expense details from a database
+        return new ValueTask<ApprovalRequest>(new ApprovalRequest(message, 1500.00m, "Jerry"));
+    }
+}
+
+/// <summary>Prepares the approval request for finance review after manager approval.</summary>
+internal sealed class PrepareFinanceReview() : Executor<ApprovalResponse, ApprovalRequest>("PrepareFinanceReview")
+{
+    public override ValueTask<ApprovalRequest> HandleAsync(
+        ApprovalResponse message,
+        IWorkflowContext context,
+        CancellationToken cancellationToken = default)
+    {
+        if (!message.Approved)
+        {
+            throw new InvalidOperationException("Cannot proceed to finance review — manager denied the expense.");
+        }
+
+        // In a real scenario, this would retrieve the original expense details
+        return new ValueTask<ApprovalRequest>(new ApprovalRequest("EXP-2025-001", 1500.00m, "Jerry"));
+    }
+}
+
+/// <summary>Processes the expense reimbursement based on the parallel approval responses.</summary>
+internal sealed class ExpenseReimburse() : Executor<ApprovalResponse[], string>("Reimburse")
+{
+    public override async ValueTask<string> HandleAsync(
+        ApprovalResponse[] message,
+        IWorkflowContext context,
+        CancellationToken cancellationToken = default)
+    {
+        // Check that all parallel approvals passed
+        ApprovalResponse? denied = Array.Find(message, r => !r.Approved);
+        if (denied is not null)
+        {
+            return $"Expense reimbursement denied. Comments: {denied.Comments}";
+        }
+
+        // Simulate payment processing
+        await Task.Delay(1000, cancellationToken);
+        return $"Expense reimbursed at {DateTime.UtcNow:O}";
+    }
+}
@@ -0,0 +1,51 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates a Human-in-the-Loop (HITL) workflow hosted in Azure Functions.
+//
+// ┌──────────────────────┐   ┌────────────────┐   ┌─────────────────────┐    ┌────────────────────┐
+// │ CreateApprovalRequest│──►│ManagerApproval │──►│PrepareFinanceReview │──┬►│  BudgetApproval    │──┐
+// └──────────────────────┘   │ (RequestPort)  │   └─────────────────────┘  │ │  (RequestPort)     │  │
+//                            └────────────────┘                            │ └────────────────────┘  │  ┌─────────────────┐
+//                                                                          │                         ├─►│ExpenseReimburse │
+//                                                                          │ ┌────────────────────┐  │  └─────────────────┘
+//                                                                          └►│ComplianceApproval  │──┘
+//                                                                            │  (RequestPort)     │
+//                                                                            └────────────────────┘
+//
+// The workflow pauses at three RequestPorts — one for the manager, then two in parallel for finance.
+// After manager approval, BudgetApproval and ComplianceApproval run concurrently via fan-out/fan-in.
+// The framework auto-generates three HTTP endpoints for each workflow:
+//   POST /api/workflows/{name}/run          - Start the workflow
+//   GET  /api/workflows/{name}/status/{id}  - Check status and pending approvals
+//   POST /api/workflows/{name}/respond/{id} - Send approval response to resume
+
+using Microsoft.Agents.AI.Hosting.AzureFunctions;
+using Microsoft.Agents.AI.Workflows;
+using Microsoft.Azure.Functions.Worker.Builder;
+using Microsoft.Extensions.Hosting;
+using WorkflowHITLFunctions;
+
+// Define executors and RequestPorts for the three HITL pause points
+CreateApprovalRequest createRequest = new();
+RequestPort<ApprovalRequest, ApprovalResponse> managerApproval = RequestPort.Create<ApprovalRequest, ApprovalResponse>("ManagerApproval");
+PrepareFinanceReview prepareFinanceReview = new();
+RequestPort<ApprovalRequest, ApprovalResponse> budgetApproval = RequestPort.Create<ApprovalRequest, ApprovalResponse>("BudgetApproval");
+RequestPort<ApprovalRequest, ApprovalResponse> complianceApproval = RequestPort.Create<ApprovalRequest, ApprovalResponse>("ComplianceApproval");
+ExpenseReimburse reimburse = new();
+
+// Build the workflow: CreateApprovalRequest -> ManagerApproval -> PrepareFinanceReview -> [BudgetApproval AND ComplianceApproval] -> ExpenseReimburse
+Workflow expenseApproval = new WorkflowBuilder(createRequest)
+    .WithName("ExpenseReimbursement")
+    .WithDescription("Expense reimbursement with manager and parallel finance approvals")
+    .AddEdge(createRequest, managerApproval)
+    .AddEdge(managerApproval, prepareFinanceReview)
+    .AddFanOutEdge(prepareFinanceReview, [budgetApproval, complianceApproval])
+    .AddFanInBarrierEdge([budgetApproval, complianceApproval], reimburse)
+    .Build();
+
+using IHost app = FunctionsApplication
+    .CreateBuilder(args)
+    .ConfigureFunctionsWebApplication()
+    .ConfigureDurableWorkflows(workflows => workflows.AddWorkflow(expenseApproval, exposeStatusEndpoint: true))
+    .Build();
+app.Run();
@@ -0,0 +1,266 @@
+# Human-in-the-Loop (HITL) Workflow — Azure Functions
+
+This sample demonstrates a durable workflow with Human-in-the-Loop support hosted in Azure Functions. The workflow pauses at three `RequestPort` nodes — one sequential manager approval, then two parallel finance approvals (budget and compliance) via fan-out/fan-in. Approval responses are sent via HTTP endpoints.
+
+## Key Concepts Demonstrated
+
+- Using multiple `RequestPort` nodes for sequential and parallel human-in-the-loop interactions in a durable workflow
+- Fan-out/fan-in pattern for parallel approval steps
+- Auto-generated HTTP endpoints for running workflows, checking status, and sending HITL responses
+- Pausing orchestrations via `WaitForExternalEvent` and resuming via `RaiseEventAsync`
+- Viewing inputs the workflow is waiting for via the status endpoint
+
+## Workflow
+
+This sample implements the following workflow:
+
+```
+┌──────────────────────┐   ┌────────────────┐   ┌─────────────────────┐    ┌────────────────────┐
+│ CreateApprovalRequest│──►│ManagerApproval │──►│PrepareFinanceReview │──┬►│  BudgetApproval    │──┐
+└──────────────────────┘   │ (RequestPort)  │   └─────────────────────┘  │ │  (RequestPort)     │  │
+                           └────────────────┘                            │ └────────────────────┘  │  ┌─────────────────┐
+                                                                         │                         ├─►│ExpenseReimburse │
+                                                                         │ ┌────────────────────┐  │  └─────────────────┘
+                                                                         └►│ComplianceApproval  │──┘
+                                                                           │  (RequestPort)     │
+                                                                           └────────────────────┘
+```
+
+## HTTP Endpoints
+
+The framework auto-generates these endpoints for workflows with `RequestPort` nodes:
+
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| POST | `/api/workflows/ExpenseReimbursement/run` | Start the workflow |
+| GET | `/api/workflows/ExpenseReimbursement/status/{runId}` | Check status and inputs the workflow is waiting for |
+| POST | `/api/workflows/ExpenseReimbursement/respond/{runId}` | Send approval response to resume |
+
+## Environment Setup
+
+See the [README.md](../../README.md) file in the parent directory for information on how to configure the environment, including how to install and run the Durable Task Scheduler.
+
+## Running the Sample
+
+With the environment setup and function app running, you can test the sample by sending HTTP requests to the workflow endpoints.
+
+You can use the `demo.http` file to trigger the workflow, or a command line tool like `curl` as shown below:
+
+### Step 1: Start the Workflow
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl -X POST http://localhost:7071/api/workflows/ExpenseReimbursement/run \
+    -H "Content-Type: text/plain" -d "EXP-2025-001"
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/workflows/ExpenseReimbursement/run `
+    -ContentType text/plain `
+    -Body "EXP-2025-001"
+```
+
+The response will confirm the workflow orchestration has started:
+
+```text
+Workflow orchestration started for ExpenseReimbursement. Orchestration runId: abc123def456
+```
+
+> [!TIP]
+> You can provide a custom run ID by appending a `runId` query parameter:
+>
+> Bash (Linux/macOS/WSL):
+>
+> ```bash
+> curl -X POST "http://localhost:7071/api/workflows/ExpenseReimbursement/run?runId=expense-001" \
+>     -H "Content-Type: text/plain" -d "EXP-2025-001"
+> ```
+>
+> PowerShell:
+>
+> ```powershell
+> Invoke-RestMethod -Method Post `
+>     -Uri "http://localhost:7071/api/workflows/ExpenseReimbursement/run?runId=expense-001" `
+>     -ContentType text/plain `
+>     -Body "EXP-2025-001"
+> ```
+>
+> If not provided, a unique run ID is auto-generated.
+
+### Step 2: Check Workflow Status
+
+The workflow pauses at the `ManagerApproval` RequestPort. Query the status endpoint to see what input it is waiting for:
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl http://localhost:7071/api/workflows/ExpenseReimbursement/status/{runId}
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Uri http://localhost:7071/api/workflows/ExpenseReimbursement/status/{runId}
+```
+
+```json
+{
+  "runId": "{runId}",
+  "status": "Running",
+  "waitingForInput": [
+    { "eventName": "ManagerApproval", "input": { "ExpenseId": "EXP-2025-001", "Amount": 1500.00, "EmployeeName": "Jerry" } }
+  ]
+}
+```
+
+> [!TIP]
+> You can also verify this in the DTS dashboard at `http://localhost:8082`. Find the orchestration by its `runId` and you will see it is in a "Running" state, paused at a `WaitForExternalEvent` call for the `ManagerApproval` event.
+
+### Step 3: Send Manager Approval Response
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl -X POST http://localhost:7071/api/workflows/ExpenseReimbursement/respond/{runId} \
+    -H "Content-Type: application/json" \
+    -d '{"eventName": "ManagerApproval", "response": {"Approved": true, "Comments": "Approved by manager."}}'
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/workflows/ExpenseReimbursement/respond/{runId} `
+    -ContentType application/json `
+    -Body '{"eventName": "ManagerApproval", "response": {"Approved": true, "Comments": "Approved by manager."}}'
+```
+
+```json
+{
+  "message": "Response sent to workflow.",
+  "runId": "{runId}",
+  "eventName": "ManagerApproval",
+  "validated": true
+}
+```
+
+### Step 4: Check Workflow Status Again
+
+The workflow now pauses at both the `BudgetApproval` and `ComplianceApproval` RequestPorts in parallel:
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl http://localhost:7071/api/workflows/ExpenseReimbursement/status/{runId}
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Uri http://localhost:7071/api/workflows/ExpenseReimbursement/status/{runId}
+```
+
+```json
+{
+  "runId": "{runId}",
+  "status": "Running",
+  "waitingForInput": [
+    { "eventName": "BudgetApproval", "input": { "ExpenseId": "EXP-2025-001", "Amount": 1500.00, "EmployeeName": "Jerry" } },
+    { "eventName": "ComplianceApproval", "input": { "ExpenseId": "EXP-2025-001", "Amount": 1500.00, "EmployeeName": "Jerry" } }
+  ]
+}
+```
+
+### Step 5a: Send Budget Approval Response
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl -X POST http://localhost:7071/api/workflows/ExpenseReimbursement/respond/{runId} \
+    -H "Content-Type: application/json" \
+    -d '{"eventName": "BudgetApproval", "response": {"Approved": true, "Comments": "Budget approved."}}'
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/workflows/ExpenseReimbursement/respond/{runId} `
+    -ContentType application/json `
+    -Body '{"eventName": "BudgetApproval", "response": {"Approved": true, "Comments": "Budget approved."}}'
+```
+
+```json
+{
+  "message": "Response sent to workflow.",
+  "runId": "{runId}",
+  "eventName": "BudgetApproval",
+  "validated": true
+}
+```
+
+### Step 5b: Send Compliance Approval Response
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl -X POST http://localhost:7071/api/workflows/ExpenseReimbursement/respond/{runId} \
+    -H "Content-Type: application/json" \
+    -d '{"eventName": "ComplianceApproval", "response": {"Approved": true, "Comments": "Compliance approved."}}'
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Method Post `
+    -Uri http://localhost:7071/api/workflows/ExpenseReimbursement/respond/{runId} `
+    -ContentType application/json `
+    -Body '{"eventName": "ComplianceApproval", "response": {"Approved": true, "Comments": "Compliance approved."}}'
+```
+
+```json
+{
+  "message": "Response sent to workflow.",
+  "runId": "{runId}",
+  "eventName": "ComplianceApproval",
+  "validated": true
+}
+```
+
+### Step 6: Check Final Status
+
+After all approvals, the workflow completes and the expense is reimbursed:
+
+Bash (Linux/macOS/WSL):
+
+```bash
+curl http://localhost:7071/api/workflows/ExpenseReimbursement/status/{runId}
+```
+
+PowerShell:
+
+```powershell
+Invoke-RestMethod -Uri http://localhost:7071/api/workflows/ExpenseReimbursement/status/{runId}
+```
+
+```json
+{
+  "runId": "{runId}",
+  "status": "Completed",
+  "waitingForInput": null
+}
+```
+
+### Viewing Workflows in the DTS Dashboard
+
+After running a workflow, you can navigate to the Durable Task Scheduler (DTS) dashboard to visualize the orchestration and inspect its execution history.
+
+If you are using the DTS emulator, the dashboard is available at `http://localhost:8082`.
+
+1. Open the dashboard and look for the orchestration instance matching the `runId` returned in Step 1 (e.g., `abc123def456` or your custom ID like `expense-001`).
+2. Click into the instance to see the execution timeline, which shows each executor activity and the `WaitForExternalEvent` pauses where the workflow waited for human input — including the two parallel finance approvals.
+3. Expand individual activity steps to inspect inputs and outputs — for example, the `ManagerApproval`, `BudgetApproval`, and `ComplianceApproval` external events will show the approval request sent and the response received.
@@ -0,0 +1,53 @@
+# Default endpoint address for local testing
+@authority=http://localhost:7071
+
+### Step 1: Start the expense reimbursement workflow
+POST {{authority}}/api/workflows/ExpenseReimbursement/run
+Content-Type: text/plain
+
+EXP-2025-001
+
+### Step 1 (alternative): Start the workflow with a custom run ID
+POST {{authority}}/api/workflows/ExpenseReimbursement/run?runId=expense-001
+Content-Type: text/plain
+
+EXP-2025-001
+
+### Step 2: Check workflow status (replace {runId} with actual run ID from Step 1)
+GET {{authority}}/api/workflows/ExpenseReimbursement/status/{runId}
+
+### Step 3: Send manager approval (replace {runId} with actual run ID from Step 1)
+POST {{authority}}/api/workflows/ExpenseReimbursement/respond/{runId}
+Content-Type: application/json
+
+{"eventName": "ManagerApproval", "response": {"Approved": true, "Comments": "Approved by manager."}}
+
+### Step 3 (alternative): Deny the expense at manager level
+POST {{authority}}/api/workflows/ExpenseReimbursement/respond/{runId}
+Content-Type: application/json
+
+{"eventName": "ManagerApproval", "response": {"Approved": false, "Comments": "Insufficient documentation. Please resubmit."}}
+
+### Step 4: Check workflow status after manager approval (now waiting for parallel finance approvals)
+GET {{authority}}/api/workflows/ExpenseReimbursement/status/{runId}
+
+### Step 5a: Send budget approval (replace {runId} with actual run ID from Step 1)
+POST {{authority}}/api/workflows/ExpenseReimbursement/respond/{runId}
+Content-Type: application/json
+
+{"eventName": "BudgetApproval", "response": {"Approved": true, "Comments": "Budget approved."}}
+
+### Step 5b: Send compliance approval (replace {runId} with actual run ID from Step 1)
+POST {{authority}}/api/workflows/ExpenseReimbursement/respond/{runId}
+Content-Type: application/json
+
+{"eventName": "ComplianceApproval", "response": {"Approved": true, "Comments": "Compliance approved."}}
+
+### Step 5b (alternative): Deny the expense at compliance level
+POST {{authority}}/api/workflows/ExpenseReimbursement/respond/{runId}
+Content-Type: application/json
+
+{"eventName": "ComplianceApproval", "response": {"Approved": false, "Comments": "Compliance requirements not met."}}
+
+### Step 6: Check final workflow status after all approvals
+GET {{authority}}/api/workflows/ExpenseReimbursement/status/{runId}
@@ -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"
+      }
+    }
+  }
+}
--- a/Show More
+++ b/Show More