chore: import upstream snapshot with attribution

dotnet/samples/02-agents/Agents/Agent_Step01_UsingFunctionToolsWithApprovals/Agent_Step01_UsingFunctionToolsWithApprovals.csproj

@@ -0,0 +1,16 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step01_UsingFunctionToolsWithApprovals/Program.cs

@@ -0,0 +1,69 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Function Tools with Approvals — Human-in-the-loop tool execution
+//
+// This sample demonstrates how to use function tools that require human
+// approval before execution. It shows both non-streaming and streaming
+// agent interactions using menu-related tools.
+// If the agent is hosted in a service, combine this with the Persisted
+// Conversations sample to persist chat history while waiting for user input.
+
+using System.ComponentModel;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using ChatMessage = Microsoft.Extensions.AI.ChatMessage;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// Create a sample function tool that the agent can use.
+[Description("Get the weather for a given location.")]
+static string GetWeather([Description("The location to get the weather for.")] string location)
+    => $"The weather in {location} is cloudy with a high of 15°C.";
+
+// Create the chat client and agent.
+// Note that we are wrapping the function tool with ApprovalRequiredAIFunction to require user approval before invoking it.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AIProjectClient(
+    new Uri(endpoint),
+    new DefaultAzureCredential())
+    .AsAIAgent(model: deploymentName, instructions: "You are a helpful assistant", tools: [new ApprovalRequiredAIFunction(AIFunctionFactory.Create(GetWeather))]);
+
+// Call the agent and check if there are any function approval requests to handle.
+// For simplicity, we are assuming here that only function approvals are pending.
+AgentSession session = await agent.CreateSessionAsync();
+AgentResponse response = await agent.RunAsync("What is the weather like in Amsterdam?", session);
+List<ToolApprovalRequestContent> approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();
+
+// For streaming use:
+// var updates = await agent.RunStreamingAsync("What is the weather like in Amsterdam?", session).ToListAsync();
+// approvalRequests = updates.SelectMany(x => x.Contents).OfType<ToolApprovalRequestContent>().ToList();
+
+while (approvalRequests.Count > 0)
+{
+    // Ask the user to approve each function call request.
+    List<ChatMessage> userInputResponses = approvalRequests
+        .ConvertAll(functionApprovalRequest =>
+        {
+            Console.WriteLine($"The agent would like to invoke the following function, please reply Y to approve: Name {((FunctionCallContent)functionApprovalRequest.ToolCall).Name}");
+            return new ChatMessage(ChatRole.User, [functionApprovalRequest.CreateResponse(Console.ReadLine()?.Equals("Y", StringComparison.OrdinalIgnoreCase) ?? false)]);
+        });
+
+    // Pass the user input responses back to the agent for further processing.
+    response = await agent.RunAsync(userInputResponses, session);
+
+    approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();
+
+    // For streaming use:
+    // updates = await agent.RunStreamingAsync(userInputResponses, session).ToListAsync();
+    // approvalRequests = updates.SelectMany(x => x.Contents).OfType<ToolApprovalRequestContent>().ToList();
+}
+
+Console.WriteLine($"\nAgent: {response}");
+
+// For streaming use:
+// Console.WriteLine($"\nAgent: {updates.ToAgentResponse()}");

dotnet/samples/02-agents/Agents/Agent_Step02_StructuredOutput/AIAgentBuilderExtensions.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.DependencyInjection;
+
+namespace SampleApp;
+
+/// <summary>
+/// Provides extension methods for adding structured output capabilities to <see cref="AIAgentBuilder"/> instances.
+/// </summary>
+internal static class AIAgentBuilderExtensions
+{
+    /// <summary>
+    /// Adds structured output capabilities to the agent pipeline, enabling conversion of text responses to structured JSON format.
+    /// </summary>
+    /// <param name="builder">The <see cref="AIAgentBuilder"/> to which structured output support will be added.</param>
+    /// <param name="chatClient">
+    /// The chat client used to transform text responses into structured JSON format.
+    /// If <see langword="null"/>, the chat client will be resolved from the service provider.
+    /// </param>
+    /// <param name="optionsFactory">
+    /// An optional factory function that returns the <see cref="StructuredOutputAgentOptions"/> instance to use.
+    /// This allows for fine-tuning the structured output behavior such as setting the response format or system message.
+    /// </param>
+    /// <returns>The <see cref="AIAgentBuilder"/> with structured output capabilities added, enabling method chaining.</returns>
+    /// <remarks>
+    /// <para>
+    /// A <see cref="ChatResponseFormatJson"/> must be specified either through the
+    /// <see cref="AgentRunOptions.ResponseFormat"/> at runtime or the <see cref="StructuredOutputAgentOptions.ChatOptions"/>
+    /// provided during configuration.
+    /// </para>
+    /// </remarks>
+    public static AIAgentBuilder UseStructuredOutput(
+        this AIAgentBuilder builder,
+        IChatClient? chatClient = null,
+        Func<StructuredOutputAgentOptions>? optionsFactory = null)
+    {
+        ArgumentNullException.ThrowIfNull(builder);
+
+        return builder.Use((innerAgent, services) =>
+        {
+            chatClient ??= services?.GetService<IChatClient>()
+                ?? throw new InvalidOperationException($"No {nameof(IChatClient)} was provided and none could be resolved from the service provider. Either provide an {nameof(IChatClient)} explicitly or register one in the dependency injection container.");
+
+            return new StructuredOutputAgent(innerAgent, chatClient, optionsFactory?.Invoke());
+        });
+    }
+}

dotnet/samples/02-agents/Agents/Agent_Step02_StructuredOutput/Agent_Step02_StructuredOutput.csproj

@@ -0,0 +1,16 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step02_StructuredOutput/Program.cs

@@ -0,0 +1,185 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Structured Output — Configure agents to return typed JSON
+//
+// This sample shows how to configure a ChatClientAgent to produce
+// structured output using JSON schema constraints with Azure AI Foundry.
+
+using System.ComponentModel;
+using System.Text.Json;
+using System.Text.Json.Serialization;
+using Azure.AI.Extensions.OpenAI;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using SampleApp;
+using ChatMessage = Microsoft.Extensions.AI.ChatMessage;
+
+string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// Create AI Project client to be used by chat client agents.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Demonstrates how to work with structured output via ResponseFormat with the non-generic RunAsync method.
+// This approach is useful when:
+// a. Structured output is used for inter-agent communication, where one agent produces structured output
+//    and passes it as text to another agent as input, without the need for the caller to directly work with the structured output.
+// b. The type of the structured output is not known at compile time, so the generic RunAsync<T> method cannot be used.
+// c. The type of the structured output is represented by JSON schema only, without a corresponding class or type in the code.
+await UseStructuredOutputWithResponseFormatAsync(aiProjectClient, deploymentName);
+
+// Demonstrates how to work with structured output via the generic RunAsync<T> method.
+// This approach is useful when the caller needs to directly work with the structured output in the code
+// via an instance of the corresponding class or type and the type is known at compile time.
+await UseStructuredOutputWithRunAsync(aiProjectClient, deploymentName);
+
+// Demonstrates how to work with structured output when streaming using the RunStreamingAsync method.
+await UseStructuredOutputWithRunStreamingAsync(aiProjectClient, deploymentName);
+
+// Demonstrates how to add structured output support to agents that don't natively support it using the structured output middleware.
+// This approach is useful when working with agents that don't support structured output natively, or agents using models
+// that don't have the capability to produce structured output, allowing you to still leverage structured output features by transforming
+// the text output from the agent into structured data using a chat client.
+await UseStructuredOutputWithMiddlewareAsync(aiProjectClient, deploymentName);
+
+static async Task UseStructuredOutputWithResponseFormatAsync(AIProjectClient aiProjectClient, string deploymentName)
+{
+    Console.WriteLine("=== Structured Output with ResponseFormat ===");
+
+    // Create the agent
+    AIAgent agent = aiProjectClient.AsAIAgent(new ChatClientAgentOptions()
+    {
+        Name = "HelpfulAssistant",
+        ChatOptions = new()
+        {
+            ModelId = deploymentName,
+            Instructions = "You are a helpful assistant.",
+            // Specify CityInfo as the type parameter of ForJsonSchema to indicate the expected structured output from the agent.
+            ResponseFormat = ChatResponseFormat.ForJsonSchema<CityInfo>()
+        }
+    });
+
+    // Invoke the agent with some unstructured input to extract the structured information from.
+    AgentResponse response = await agent.RunAsync("Provide information about the capital of France.");
+
+    // Access the structured output via the Text property of the agent response as JSON in scenarios when JSON as text is required
+    // and no object instance is needed (e.g., for logging, forwarding to another service, or storing in a database).
+    Console.WriteLine("Assistant Output (JSON):");
+    Console.WriteLine(response.Text);
+    Console.WriteLine();
+
+    // Deserialize the JSON text to work with the structured object in scenarios when you need to access properties,
+    // perform operations, or pass the data to methods that require the typed object instance.
+    CityInfo cityInfo = JsonSerializer.Deserialize<CityInfo>(response.Text)!;
+
+    Console.WriteLine("Assistant Output (Deserialized):");
+    Console.WriteLine($"Name: {cityInfo.Name}");
+    Console.WriteLine();
+}
+
+static async Task UseStructuredOutputWithRunAsync(AIProjectClient aiProjectClient, string deploymentName)
+{
+    Console.WriteLine("=== Structured Output with RunAsync<T> ===");
+
+    // Create the agent
+    AIAgent agent = aiProjectClient.AsAIAgent(deploymentName, name: "HelpfulAssistant", instructions: "You are a helpful assistant.");
+
+    // Set CityInfo as the type parameter of RunAsync method to specify the expected structured output from the agent and invoke it with some unstructured input.
+    AgentResponse<CityInfo> response = await agent.RunAsync<CityInfo>("Provide information about the capital of France.");
+
+    // Access the structured output via the Result property of the agent response.
+    CityInfo cityInfo = response.Result;
+
+    Console.WriteLine("Assistant Output:");
+    Console.WriteLine($"Name: {cityInfo.Name}");
+    Console.WriteLine();
+}
+
+static async Task UseStructuredOutputWithRunStreamingAsync(AIProjectClient aiProjectClient, string deploymentName)
+{
+    Console.WriteLine("=== Structured Output with RunStreamingAsync ===");
+
+    // Create the agent
+    AIAgent agent = aiProjectClient.AsAIAgent(new ChatClientAgentOptions()
+    {
+        Name = "HelpfulAssistant",
+        ChatOptions = new()
+        {
+            ModelId = deploymentName,
+            Instructions = "You are a helpful assistant.",
+            // Specify CityInfo as the type parameter of ForJsonSchema to indicate the expected structured output from the agent.
+            ResponseFormat = ChatResponseFormat.ForJsonSchema<CityInfo>()
+        }
+    });
+
+    // Invoke the agent with some unstructured input while streaming, to extract the structured information from.
+    IAsyncEnumerable<AgentResponseUpdate> updates = agent.RunStreamingAsync("Provide information about the capital of France.");
+
+    // Assemble all the parts of the streamed output.
+    AgentResponse nonGenericResponse = await updates.ToAgentResponseAsync();
+
+    // Access the structured output by deserializing JSON in the Text property.
+    CityInfo cityInfo = JsonSerializer.Deserialize<CityInfo>(nonGenericResponse.Text)!;
+
+    Console.WriteLine("Assistant Output:");
+    Console.WriteLine($"Name: {cityInfo.Name}");
+    Console.WriteLine();
+}
+
+static async Task UseStructuredOutputWithMiddlewareAsync(AIProjectClient aiProjectClient, string deploymentName)
+{
+    Console.WriteLine("=== Structured Output with UseStructuredOutput Middleware ===");
+
+    // Create chat client that will transform the agent text response into structured output.
+    IChatClient meaiChatClient = aiProjectClient.GetProjectOpenAIClient().GetProjectResponsesClientForModel(deploymentName).AsIChatClientWithStoredOutputDisabled(deploymentName);
+
+    // Create the agent
+    AIAgent agent = meaiChatClient.AsAIAgent(name: "HelpfulAssistant", instructions: "You are a helpful assistant.");
+
+    // Add structured output middleware via UseStructuredOutput method to add structured output support to the agent.
+    // This middleware transforms the agent's text response into structured data using a chat client.
+    // Since our agent does support structured output natively, we will add a middleware that removes ResponseFormat
+    //  from the AgentRunOptions to emulate an agent that doesn't support structured output natively
+    agent = agent
+        .AsBuilder()
+        .UseStructuredOutput(meaiChatClient)
+        .Use(ResponseFormatRemovalMiddleware, null)
+        .Build();
+
+    // Set CityInfo as the type parameter of RunAsync method to specify the expected structured output from the agent and invoke it with some unstructured input.
+    AgentResponse<CityInfo> response = await agent.RunAsync<CityInfo>("Provide information about the capital of France.");
+
+    // Access the structured output via the Result property of the agent response.
+    CityInfo cityInfo = response.Result;
+
+    Console.WriteLine("Assistant Output:");
+    Console.WriteLine($"Name: {cityInfo.Name}");
+    Console.WriteLine();
+}
+
+static Task<AgentResponse> ResponseFormatRemovalMiddleware(IEnumerable<ChatMessage> messages, AgentSession? session, AgentRunOptions? options, AIAgent innerAgent, CancellationToken cancellationToken)
+{
+    // Remove any ResponseFormat from the options to emulate an agent that doesn't support structured output natively.
+    options = options?.Clone();
+    options?.ResponseFormat = null;
+
+    return innerAgent.RunAsync(messages, session, options, cancellationToken);
+}
+
+namespace SampleApp
+{
+    /// <summary>
+    /// Represents information about a city, including its name.
+    /// </summary>
+    [Description("Information about a city")]
+    public sealed class CityInfo
+    {
+        [JsonPropertyName("name")]
+        public string? Name { get; set; }
+    }
+}

dotnet/samples/02-agents/Agents/Agent_Step02_StructuredOutput/README.md

@@ -0,0 +1,52 @@
+# Structured Output with ChatClientAgent
+
+This sample demonstrates how to configure ChatClientAgent to produce structured output in JSON format using various approaches.
+
+## What this sample demonstrates
+
+- **ResponseFormat approach**: Configuring agents with JSON schema response format via `ChatResponseFormat.ForJsonSchema<T>()` for inter-agent communication or when the type is not known at compile time
+- **Generic RunAsync<T> method**: Using the generic `RunAsync<T>` method for structured output when the caller needs to work directly with typed objects
+- **Structured output with Streaming**: Using `RunStreamingAsync` to stream responses while still obtaining structured output by assembling and deserializing the streamed content
+- **StructuredOutput middleware**: Adding structured output support to agents that don't natively support it (like A2A agents or models without structured output capability) by transforming text output into structured data using a chat client
+
+## Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure OpenAI service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+- User has the `Cognitive Services OpenAI Contributor` role for the Azure OpenAI resource
+
+**Note**: This sample uses Azure OpenAI models. For more information, see [how to deploy Azure OpenAI models with Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/deploy-models-openai).
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource and have the `Cognitive Services OpenAI Contributor` role. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+## Environment Variables
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
+```
+
+## Run the sample
+
+Navigate to the sample directory and run:
+
+```powershell
+cd dotnet/samples/02-agents/Agents/Agent_Step02_StructuredOutput
+dotnet run
+```
+
+## Expected behavior
+
+The sample will demonstrate four different approaches to structured output:
+
+1. **Structured Output with ResponseFormat**: Creates an agent with `ResponseFormat` set to `ForJsonSchema<CityInfo>()`, invokes it with unstructured input, and accesses the structured output via the `Text` property
+2. **Structured Output with RunAsync<T>**: Creates an agent and uses the generic `RunAsync<CityInfo>()` method to get a typed `AgentResponse<CityInfo>` with the result accessible via the `Result` property
+3. **Structured Output with RunStreamingAsync**: Creates an agent with JSON schema response format, streams the response using `RunStreamingAsync`, assembles the updates using `ToAgentResponseAsync()`, and deserializes the JSON text into a typed object
+4. **Structured Output with StructuredOutput Middleware**: Uses the `UseStructuredOutput` method on `AIAgentBuilder` to add structured output support to agents that don't natively support it
+
+Each approach will output information about the capital of France (Paris) in a structured format.

dotnet/samples/02-agents/Agents/Agent_Step02_StructuredOutput/StructuredOutputAgent.cs

@@ -0,0 +1,88 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+namespace SampleApp;
+
+/// <summary>
+/// A delegating AI agent that converts text responses from an inner AI agent into structured output using a chat client.
+/// </summary>
+/// <remarks>
+/// <para>
+/// The <see cref="StructuredOutputAgent"/> wraps an inner agent and uses a chat client to transform
+/// the inner agent's text response into a structured JSON format based on the specified response format.
+/// </para>
+/// <para>
+/// This agent requires a <see cref="ChatResponseFormatJson"/> to be specified either through the
+/// <see cref="AgentRunOptions.ResponseFormat"/> or the <see cref="StructuredOutputAgentOptions.ChatOptions"/>
+/// provided during construction.
+/// </para>
+/// </remarks>
+internal sealed class StructuredOutputAgent : DelegatingAIAgent
+{
+    private readonly IChatClient _chatClient;
+    private readonly StructuredOutputAgentOptions? _agentOptions;
+
+    /// <summary>
+    /// Initializes a new instance of the <see cref="StructuredOutputAgent"/> class.
+    /// </summary>
+    /// <param name="innerAgent">The underlying agent that generates text responses to be converted to structured output.</param>
+    /// <param name="chatClient">The chat client used to transform text responses into structured JSON format.</param>
+    /// <param name="options">Optional configuration options for the structured output agent.</param>
+    public StructuredOutputAgent(AIAgent innerAgent, IChatClient chatClient, StructuredOutputAgentOptions? options = null)
+        : base(innerAgent)
+    {
+        this._chatClient = chatClient ?? throw new ArgumentNullException(nameof(chatClient));
+        this._agentOptions = options;
+    }
+
+    /// <inheritdoc />
+    protected override async Task<AgentResponse> RunCoreAsync(
+        IEnumerable<ChatMessage> messages,
+        AgentSession? session = null,
+        AgentRunOptions? options = null,
+        CancellationToken cancellationToken = default)
+    {
+        // Run the inner agent first, to get back the text response we want to convert.
+        var textResponse = await this.InnerAgent.RunAsync(messages, session, options, cancellationToken).ConfigureAwait(false);
+
+        // Invoke the chat client to transform the text output into structured data.
+        ChatResponse soResponse = await this._chatClient.GetResponseAsync(
+            messages: this.GetChatMessages(textResponse.Text),
+            options: this.GetChatOptions(options),
+            cancellationToken: cancellationToken).ConfigureAwait(false);
+
+        return new StructuredOutputAgentResponse(soResponse, textResponse);
+    }
+
+    private List<ChatMessage> GetChatMessages(string? textResponseText)
+    {
+        List<ChatMessage> chatMessages = [];
+
+        if (this._agentOptions?.ChatClientSystemMessage is not null)
+        {
+            chatMessages.Add(new ChatMessage(ChatRole.System, this._agentOptions.ChatClientSystemMessage));
+        }
+
+        chatMessages.Add(new ChatMessage(ChatRole.User, textResponseText));
+
+        return chatMessages;
+    }
+
+    private ChatOptions GetChatOptions(AgentRunOptions? options)
+    {
+        ChatResponseFormat responseFormat = options?.ResponseFormat
+            ?? this._agentOptions?.ChatOptions?.ResponseFormat
+            ?? throw new InvalidOperationException($"A response format of type '{nameof(ChatResponseFormatJson)}' must be specified, but none was specified.");
+
+        if (responseFormat is not ChatResponseFormatJson jsonResponseFormat)
+        {
+            throw new NotSupportedException($"A response format of type '{nameof(ChatResponseFormatJson)}' must be specified, but was '{responseFormat.GetType().Name}'.");
+        }
+
+        var chatOptions = this._agentOptions?.ChatOptions?.Clone() ?? new ChatOptions();
+        chatOptions.ResponseFormat = jsonResponseFormat;
+        return chatOptions;
+    }
+}

dotnet/samples/02-agents/Agents/Agent_Step02_StructuredOutput/StructuredOutputAgentOptions.cs

@@ -0,0 +1,31 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+namespace SampleApp;
+
+/// <summary>
+/// Represents configuration options for a <see cref="StructuredOutputAgent"/>.
+/// </summary>
+#pragma warning disable CA1812 // Instantiated via AIAgentBuilderExtensions.UseStructuredOutput optionsFactory parameter
+internal sealed class StructuredOutputAgentOptions
+#pragma warning restore CA1812
+{
+    /// <summary>
+    /// Gets or sets the system message to use when invoking the chat client for structured output conversion.
+    /// </summary>
+    public string? ChatClientSystemMessage { get; set; }
+
+    /// <summary>
+    /// Gets or sets the chat options to use for the structured output conversion by the chat client
+    /// used by the agent.
+    /// </summary>
+    /// <remarks>
+    /// This property is optional. The <see cref="ChatOptions.ResponseFormat"/> should be set to a
+    /// <see cref="ChatResponseFormatJson"/> instance to specify the expected JSON schema for the structured output.
+    /// Note that if <see cref="AgentRunOptions.ResponseFormat"/> is provided when running the agent,
+    /// it will take precedence and override the <see cref="ChatOptions.ResponseFormat"/> specified here.
+    /// </remarks>
+    public ChatOptions? ChatOptions { get; set; }
+}

dotnet/samples/02-agents/Agents/Agent_Step02_StructuredOutput/StructuredOutputAgentResponse.cs

@@ -0,0 +1,28 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+namespace SampleApp;
+
+/// <summary>
+/// Represents an agent response that contains structured output and
+/// the original agent response from which the structured output was generated.
+/// </summary>
+internal sealed class StructuredOutputAgentResponse : AgentResponse
+{
+    /// <summary>
+    /// Initializes a new instance of the <see cref="StructuredOutputAgentResponse"/> class.
+    /// </summary>
+    /// <param name="chatResponse">The <see cref="ChatResponse"/> containing the structured output.</param>
+    /// <param name="agentResponse">The original <see cref="AgentResponse"/> from the inner agent.</param>
+    public StructuredOutputAgentResponse(ChatResponse chatResponse, AgentResponse agentResponse) : base(chatResponse)
+    {
+        this.OriginalResponse = agentResponse;
+    }
+
+    /// <summary>
+    /// Gets the original non-structured response from the inner agent used by chat client to produce the structured output.
+    /// </summary>
+    public AgentResponse OriginalResponse { get; }
+}

dotnet/samples/02-agents/Agents/Agent_Step03_PersistedConversations/Agent_Step03_PersistedConversations.csproj

@@ -0,0 +1,16 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step03_PersistedConversations/Program.cs

@@ -0,0 +1,46 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+#pragma warning disable CA1869 // Cache and reuse 'JsonSerializerOptions' instances
+
+// Persisted Conversations — Save and restore chat history to disk
+//
+// This sample shows how to persist an agent conversation to disk
+// so it can be resumed across process restarts.
+
+using System.Text.Json;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// Create the agent
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AIProjectClient(
+    new Uri(endpoint),
+    new DefaultAzureCredential())
+    .AsAIAgent(model: deploymentName, instructions: "You are good at telling jokes.", name: "Joker");
+
+// Start a new session for the agent conversation.
+AgentSession session = await agent.CreateSessionAsync();
+
+// Run the agent with a new session.
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", session));
+
+// Serialize the session state to a JsonElement, so it can be stored for later use.
+JsonElement serializedSession = await agent.SerializeSessionAsync(session);
+
+// In a real application, you would typically write the serialized session to a file or
+// database for persistence, and read it back when resuming the conversation.
+// Here we'll just write the serialized session to console (for demonstration purposes).
+Console.WriteLine("\n--- Serialized session ---\n");
+Console.WriteLine(JsonSerializer.Serialize(serializedSession, new JsonSerializerOptions { WriteIndented = true }) + "\n");
+
+// Deserialize the session state after loading from storage.
+AgentSession resumedSession = await agent.DeserializeSessionAsync(serializedSession);
+
+// Run the agent again with the resumed session.
+Console.WriteLine(await agent.RunAsync("Now tell the same joke in the voice of a pirate, and add some emojis to the joke.", resumedSession));

dotnet/samples/02-agents/Agents/Agent_Step04_3rdPartyChatHistoryStorage/Agent_Step04_3rdPartyChatHistoryStorage.csproj

@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step04_3rdPartyChatHistoryStorage/Program.cs

@@ -0,0 +1,176 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+#pragma warning disable CA1869 // Cache and reuse 'JsonSerializerOptions' instances
+
+// Third-Party Chat History Storage — Custom ChatHistoryProvider
+//
+// This sample shows how to use a custom ChatHistoryProvider that stores
+// chat history in an external location. The provider's state (SessionDbKey)
+// is stored in AgentSession.StateBag so conversations can be resumed later.
+
+using System.Text.Json;
+using Azure.AI.Extensions.OpenAI;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.VectorData;
+using Microsoft.SemanticKernel.Connectors.InMemory;
+using SampleApp;
+using ChatMessage = Microsoft.Extensions.AI.ChatMessage;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// Create a vector store to store the chat messages in.
+// Replace this with a vector store implementation of your choice if you want to persist the chat history to disk.
+VectorStore vectorStore = new InMemoryVectorStore();
+
+// Create the agent
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AIProjectClient(
+    new Uri(endpoint),
+    new DefaultAzureCredential())
+    .GetProjectOpenAIClient()
+    .GetProjectResponsesClient()
+    .AsIChatClientWithStoredOutputDisabled(deploymentName)
+    .AsAIAgent(new ChatClientAgentOptions
+    {
+        ChatOptions = new() { ModelId = deploymentName, Instructions = "You are good at telling jokes." },
+        Name = "Joker",
+        // Create a new ChatHistoryProvider for this agent that stores chat history in a vector store.
+        ChatHistoryProvider = new VectorChatHistoryProvider(vectorStore),
+    });
+
+// Start a new session for the agent conversation.
+AgentSession session = await agent.CreateSessionAsync();
+
+// Run the agent with the session that stores chat history in the vector store.
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", session));
+
+// Serialize the session state, so it can be stored for later use.
+// Since the chat history is stored in the vector store, the serialized session
+// only contains the guid that the messages are stored under in the vector store.
+JsonElement serializedSession = await agent.SerializeSessionAsync(session);
+
+Console.WriteLine("\n--- Serialized session ---\n");
+Console.WriteLine(JsonSerializer.Serialize(serializedSession, new JsonSerializerOptions { WriteIndented = true }));
+
+// The serialized session can now be saved to a database, file, or any other storage mechanism
+// and loaded again later.
+
+// Deserialize the session state after loading from storage.
+AgentSession resumedSession = await agent.DeserializeSessionAsync(serializedSession);
+
+// Run the agent with the session that stores chat history in the vector store a second time.
+Console.WriteLine(await agent.RunAsync("Now tell the same joke in the voice of a pirate, and add some emojis to the joke.", resumedSession));
+
+// We can access the VectorChatHistoryProvider via the agent's GetService method
+// if we need to read the key under which chat history is stored. The key is stored
+// in the session state, and therefore we need to provide the session when reading it.
+var chatHistoryProvider = agent.GetService<VectorChatHistoryProvider>()!;
+Console.WriteLine($"\nSession is stored in vector store under key: {chatHistoryProvider.GetSessionDbKey(resumedSession)}");
+
+namespace SampleApp
+{
+    /// <summary>
+    /// A sample implementation of <see cref="ChatHistoryProvider"/> that stores chat history in a vector store.
+    /// State (the session DB key) is stored in the <see cref="AgentSession.StateBag"/> so it roundtrips
+    /// automatically with session serialization.
+    /// </summary>
+    internal sealed class VectorChatHistoryProvider : ChatHistoryProvider
+    {
+        private readonly ProviderSessionState<State> _sessionState;
+        private IReadOnlyList<string>? _stateKeys;
+        private readonly VectorStore _vectorStore;
+
+        public VectorChatHistoryProvider(
+            VectorStore vectorStore,
+            Func<AgentSession?, State>? stateInitializer = null,
+            string? stateKey = null)
+        {
+            this._sessionState = new ProviderSessionState<State>(
+                stateInitializer ?? (_ => new State(Guid.NewGuid().ToString("N"))),
+                stateKey ?? this.GetType().Name);
+            this._vectorStore = vectorStore ?? throw new ArgumentNullException(nameof(vectorStore));
+        }
+
+        public override IReadOnlyList<string> StateKeys => this._stateKeys ??= [this._sessionState.StateKey];
+
+        public string GetSessionDbKey(AgentSession session)
+            => this._sessionState.GetOrInitializeState(session).SessionDbKey;
+
+        protected override async ValueTask<IEnumerable<ChatMessage>> ProvideChatHistoryAsync(InvokingContext context, CancellationToken cancellationToken = default)
+        {
+            var state = this._sessionState.GetOrInitializeState(context.Session);
+            var collection = this._vectorStore.GetCollection<string, ChatHistoryItem>("ChatHistory");
+            await collection.EnsureCollectionExistsAsync(cancellationToken);
+
+            var records = await collection
+                .GetAsync(
+                    x => x.SessionId == state.SessionDbKey, 10,
+                    new() { OrderBy = x => x.Descending(y => y.Timestamp) },
+                    cancellationToken)
+                .ToListAsync(cancellationToken);
+
+            var messages = records.ConvertAll(x => JsonSerializer.Deserialize<ChatMessage>(x.SerializedMessage!)!);
+            messages.Reverse();
+            return messages;
+        }
+
+        protected override async ValueTask StoreChatHistoryAsync(InvokedContext context, CancellationToken cancellationToken = default)
+        {
+            var state = this._sessionState.GetOrInitializeState(context.Session);
+
+            var collection = this._vectorStore.GetCollection<string, ChatHistoryItem>("ChatHistory");
+            await collection.EnsureCollectionExistsAsync(cancellationToken);
+
+            var allNewMessages = context.RequestMessages.Concat(context.ResponseMessages ?? []);
+
+            await collection.UpsertAsync(allNewMessages.Select(x => new ChatHistoryItem()
+            {
+                Key = state.SessionDbKey + x.MessageId,
+                Timestamp = DateTimeOffset.UtcNow,
+                SessionId = state.SessionDbKey,
+                SerializedMessage = JsonSerializer.Serialize(x),
+                MessageText = x.Text
+            }), cancellationToken);
+        }
+
+        /// <summary>
+        /// Represents the per-session state stored in the <see cref="AgentSession.StateBag"/>.
+        /// </summary>
+        public sealed class State
+        {
+            public State(string sessionDbKey)
+            {
+                this.SessionDbKey = sessionDbKey ?? throw new ArgumentNullException(nameof(sessionDbKey));
+            }
+
+            public string SessionDbKey { get; }
+        }
+
+        /// <summary>
+        /// The data structure used to store chat history items in the vector store.
+        /// </summary>
+        private sealed class ChatHistoryItem
+        {
+            [VectorStoreKey]
+            public string? Key { get; set; }
+
+            [VectorStoreData]
+            public string? SessionId { get; set; }
+
+            [VectorStoreData]
+            public DateTimeOffset? Timestamp { get; set; }
+
+            [VectorStoreData]
+            public string? SerializedMessage { get; set; }
+
+            [VectorStoreData]
+            public string? MessageText { get; set; }
+        }
+    }
+}

dotnet/samples/02-agents/Agents/Agent_Step05_Observability/Agent_Step05_Observability.csproj

@@ -0,0 +1,22 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Azure.Monitor.OpenTelemetry.Exporter" />
+    <PackageReference Include="OpenTelemetry" />
+    <PackageReference Include="OpenTelemetry.Exporter.Console" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step05_Observability/Program.cs

@@ -0,0 +1,48 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Agent Observability — OpenTelemetry tracing with Azure AI Foundry
+//
+// This sample shows how to instrument an AI agent with OpenTelemetry
+// for distributed tracing and telemetry logging.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Azure.Monitor.OpenTelemetry.Exporter;
+using Microsoft.Agents.AI;
+using OpenTelemetry;
+using OpenTelemetry.Trace;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+var applicationInsightsConnectionString = Environment.GetEnvironmentVariable("APPLICATIONINSIGHTS_CONNECTION_STRING");
+
+// Create TracerProvider with console exporter
+// This will output the telemetry data to the console.
+string sourceName = Guid.NewGuid().ToString("N");
+var tracerProviderBuilder = Sdk.CreateTracerProviderBuilder()
+    .AddSource(sourceName)
+    .AddConsoleExporter();
+if (!string.IsNullOrWhiteSpace(applicationInsightsConnectionString))
+{
+    tracerProviderBuilder.AddAzureMonitorTraceExporter(options => options.ConnectionString = applicationInsightsConnectionString);
+}
+using var tracerProvider = tracerProviderBuilder.Build();
+
+// Create the agent, and enable OpenTelemetry instrumentation.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
+    .AsAIAgent(model: deploymentName, instructions: "You are good at telling jokes.", name: "Joker")
+    .AsBuilder()
+    .UseOpenTelemetry(sourceName: sourceName)
+    .Build();
+
+// Invoke the agent and output the text result.
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate."));
+
+// Invoke the agent with streaming support.
+await foreach (var update in agent.RunStreamingAsync("Tell me a joke about a pirate."))
+{
+    Console.WriteLine(update);
+}

dotnet/samples/02-agents/Agents/Agent_Step06_DependencyInjection/Agent_Step06_DependencyInjection.csproj

@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.Hosting" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step06_DependencyInjection/Program.cs

@@ -0,0 +1,80 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+#pragma warning disable CA1812
+
+// Dependency Injection — Register and resolve agents via DI
+//
+// This sample shows how to use dependency injection to register an
+// AIAgent and consume it from a hosted service with a chat loop.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// Create a host builder that we will register services with and then run.
+HostApplicationBuilder builder = Host.CreateApplicationBuilder(args);
+
+// Create the AI agent from the Azure AI Foundry project client.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+AIAgent agent = aiProjectClient.AsAIAgent(model: deploymentName, name: "Joker", instructions: "You are good at telling jokes.");
+builder.Services.AddSingleton(agent);
+
+// Add a sample service that will use the agent to respond to user input.
+builder.Services.AddHostedService<SampleService>();
+
+// Build and run the host.
+using IHost host = builder.Build();
+await host.RunAsync().ConfigureAwait(false);
+
+/// <summary>
+/// A sample service that uses an AI agent to respond to user input.
+/// </summary>
+internal sealed class SampleService(AIAgent agent, IHostApplicationLifetime appLifetime) : IHostedService
+{
+    private AgentSession? _session;
+
+    public async Task StartAsync(CancellationToken cancellationToken)
+    {
+        // Create a session that will be used for the entirety of the service lifetime so that the user can ask follow up questions.
+        this._session = await agent.CreateSessionAsync(cancellationToken);
+        _ = this.RunAsync(appLifetime.ApplicationStopping);
+    }
+
+    public async Task RunAsync(CancellationToken cancellationToken)
+    {
+        // Delay a little to allow the service to finish starting.
+        await Task.Delay(100, cancellationToken);
+
+        while (!cancellationToken.IsCancellationRequested)
+        {
+            Console.WriteLine("\nAgent: Ask me to tell you a joke about a specific topic. To exit just press Ctrl+C or enter without any input.\n");
+            Console.Write("> ");
+            var input = Console.ReadLine();
+
+            // If the user enters no input, signal the application to shut down.
+            if (string.IsNullOrWhiteSpace(input))
+            {
+                appLifetime.StopApplication();
+                break;
+            }
+
+            // Stream the output to the console as it is generated.
+            await foreach (var update in agent.RunStreamingAsync(input, this._session, cancellationToken: cancellationToken))
+            {
+                Console.Write(update);
+            }
+
+            Console.WriteLine();
+        }
+    }
+
+    public Task StopAsync(CancellationToken cancellationToken) => Task.CompletedTask;
+}

dotnet/samples/02-agents/Agents/Agent_Step07_AsMcpTool/Agent_Step07_AsMcpTool.csproj

@@ -0,0 +1,23 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <UserSecretsId>3afc9b74-af74-4d8e-ae96-fa1c511d11ac</UserSecretsId>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.Projects" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.Hosting" />
+    <PackageReference Include="ModelContextProtocol" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+  
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step07_AsMcpTool/Program.cs

@@ -0,0 +1,45 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to expose an AI agent as an MCP tool.
+
+using Azure.AI.Projects;
+using Azure.AI.Projects.Agents;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+using ModelContextProtocol.Server;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+var aiProjectClient = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential());
+
+// Create a server side agent and expose it as an AIAgent.
+ProjectsAgentVersion agentVersion = await aiProjectClient.AgentAdministrationClient.CreateAgentVersionAsync(
+    "Joker",
+    new ProjectsAgentVersionCreationOptions(
+        new DeclarativeAgentDefinition(model: deploymentName)
+        {
+            Instructions = "You are good at telling jokes, and you always start each joke with 'Aye aye, captain!'.",
+        })
+    {
+        Description = "An agent that tells jokes.",
+    });
+AIAgent agent = aiProjectClient.AsAIAgent(agentVersion);
+
+// Convert the agent to an AIFunction and then to an MCP tool.
+// The agent name and description will be used as the mcp tool name and description.
+McpServerTool tool = McpServerTool.Create(agent.AsAIFunction());
+
+// Register the MCP server with StdIO transport and expose the tool via the server.
+HostApplicationBuilder builder = Host.CreateEmptyApplicationBuilder(settings: null);
+builder.Services
+    .AddMcpServer()
+    .WithStdioServerTransport()
+    .WithTools([tool]);
+
+await builder.Build().RunAsync();

dotnet/samples/02-agents/Agents/Agent_Step07_AsMcpTool/README.md

@@ -0,0 +1,29 @@
+This sample demonstrates how to expose an existing AI agent as an MCP tool.
+
+## Run the sample
+
+To run the sample, please use one of the following MCP clients: https://modelcontextprotocol.io/clients
+
+Alternatively, use the QuickstartClient sample from this repository: https://github.com/modelcontextprotocol/csharp-sdk/tree/main/samples/QuickstartClient
+
+## Run the sample using MCP Inspector
+
+To use the [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector), follow these steps:
+
+1. Open a terminal in the Agent_Step07_AsMcpTool project directory.
+1. Run the `npx @modelcontextprotocol/inspector dotnet run --framework net10.0` command to start the MCP Inspector. Make sure you have [node.js](https://nodejs.org/en/download/) and npm installed.
+   ```bash
+   npx @modelcontextprotocol/inspector dotnet run --framework net10.0
+   ```
+1. When the inspector is running, it will display a URL in the terminal, like this:
+   ```
+   MCP Inspector is up and running at http://127.0.0.1:6274
+   ```
+1. Open a web browser and navigate to the URL displayed in the terminal. If not opened automatically, this will open the MCP Inspector interface.
+1. In the MCP Inspector interface, add the following environment variables to allow your MCP server to access Microsoft Foundry Project to create and run the agent:
+    - FOUNDRY_PROJECT_ENDPOINT = https://your-resource.openai.azure.com/ # Replace with your Microsoft Foundry Project endpoint
+    - FOUNDRY_MODEL = gpt-5.4-mini # Replace with your model deployment name
+1. Find and click the `Connect` button in the MCP Inspector interface to connect to the MCP server.
+1. As soon as the connection is established, open the `Tools` tab in the MCP Inspector interface and select the `Joker` tool from the list.
+1. Specify your prompt as a value for the `query` argument, for example: `Tell me a joke about a pirate` and click the `Run Tool` button to run the tool.
+1. The agent will process the request and return a response in accordance with the provided instructions that instruct it to always start each joke with 'Aye aye, captain!'.

dotnet/samples/02-agents/Agents/Agent_Step08_UsingImages/Agent_Step08_UsingImages.csproj

@@ -0,0 +1,22 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <None Update="Assets\walkway.jpg">
+      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
+    </None>
+  </ItemGroup>
+  
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step08_UsingImages/Program.cs

@@ -0,0 +1,34 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Using Images — Multimodal input with an AI agent
+//
+// This sample shows how to send image content to an AI agent
+// for vision-based analysis.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Extensions.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = System.Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+var agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
+    .AsAIAgent(
+        model: deploymentName,
+        instructions: "You are a helpful agent that can analyze images",
+        name: "VisionAgent");
+
+ChatMessage message = new(ChatRole.User, [
+    new TextContent("What do you see in this image?"),
+    await DataContent.LoadFromAsync("Assets/walkway.jpg"),
+]);
+
+var session = await agent.CreateSessionAsync();
+
+await foreach (var update in agent.RunStreamingAsync(message, session))
+{
+    Console.WriteLine(update);
+}

dotnet/samples/02-agents/Agents/Agent_Step08_UsingImages/README.md

@@ -0,0 +1,51 @@
+# Using Images with AI Agents
+
+This sample demonstrates how to use image multi-modality with an AI agent. It shows how to create a vision-enabled agent that can analyze and describe images using Microsoft Foundry with `AIProjectClient`.
+
+## What this sample demonstrates
+
+- Creating a persistent AI agent with vision capabilities
+- Sending both text and image content to an agent in a single message
+- Using `UriContent` to Uri referenced images
+- Processing multimodal input (text + image) with an AI agent
+
+## Key features
+
+- **Vision Agent**: Creates an agent specifically instructed to analyze images
+- **Multimodal Input**: Combines text questions with image uri in a single message
+- **Microsoft Foundry Integration**: Uses `AIProjectClient` to create a Foundry-backed agent
+
+## Prerequisites
+
+Before running this sample, ensure you have:
+
+1. A Microsoft Foundry project set up
+2. A compatible model deployment (e.g., gpt-5.4-mini)
+3. Azure CLI installed and authenticated
+
+## Environment Variables
+
+Set the following environment variables:
+
+```powershell
+$env:FOUNDRY_PROJECT_ENDPOINT="https://<your-project>.services.ai.azure.com/api/projects/<your-project>" # Replace with your Foundry project endpoint
+$env:FOUNDRY_MODEL="gpt-5.4-mini" # Replace with your model name (optional, defaults to gpt-5.4-mini)
+```
+
+## Run the sample
+
+Navigate to the sample directory and run:
+
+```powershell
+cd Agent_Step08_UsingImages
+dotnet run
+```
+
+## Expected behavior
+
+The sample will:
+
+1. Create a vision-enabled agent named "VisionAgent"
+2. Send a message containing both text ("What do you see in this image?") and a Uri image of a green walk
+3. The agent will analyze the image and provide a description
+4. Clean up resources by deleting the thread and agent

dotnet/samples/02-agents/Agents/Agent_Step09_AsFunctionTool/Agent_Step09_AsFunctionTool.csproj

@@ -0,0 +1,21 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <UserSecretsId>3afc9b74-af74-4d8e-ae96-fa1c511d11ac</UserSecretsId>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.Hosting" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+  
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step09_AsFunctionTool/Program.cs

@@ -0,0 +1,43 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Agent as Function Tool — Use one agent as a tool for another
+//
+// This sample shows how to create an AI agent and expose it as a
+// function tool that another agent can call.
+
+using System.ComponentModel;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+[Description("Get the weather for a given location.")]
+static string GetWeather([Description("The location to get the weather for.")] string location)
+    => $"The weather in {location} is cloudy with a high of 15°C.";
+
+// Create the agent and provide the function tool to it.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+AIAgent weatherAgent = aiProjectClient
+     .AsAIAgent(
+        model: deploymentName,
+        instructions: "You answer questions about the weather.",
+        name: "WeatherAgent",
+        description: "An agent that answers questions about the weather.",
+        tools: [AIFunctionFactory.Create(GetWeather)]);
+
+// Create the main agent, and provide the weather agent as a function tool.
+AIAgent agent = aiProjectClient
+    .AsAIAgent(
+        model: deploymentName,
+        instructions: "You are a helpful assistant who responds in French.",
+        tools: [weatherAgent.AsAIFunction()]);
+
+// Invoke the agent and output the text result.
+Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?"));

dotnet/samples/02-agents/Agents/Agent_Step10_BackgroundResponsesWithToolsAndPersistence/Agent_Step10_BackgroundResponsesWithToolsAndPersistence.csproj

@@ -0,0 +1,16 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+ <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+  
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step10_BackgroundResponsesWithToolsAndPersistence/Program.cs

@@ -0,0 +1,115 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Background Responses with Tools — Long-running operations with persistence
+//
+// This sample demonstrates how to use background responses with ChatClientAgent
+// for long-running operations. It shows polling for completion using continuation
+// tokens, function calling during background operations, and persisting/restoring
+// agent state between polling cycles.
+
+#pragma warning disable CA1050 // Declare types in namespaces
+
+using System.ComponentModel;
+using System.Text.Json;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+var stateStore = new Dictionary<string, JsonElement?>();
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AIProjectClient(
+    new Uri(endpoint),
+    new DefaultAzureCredential())
+     .AsAIAgent(
+        model: deploymentName,
+        name: "SpaceNovelWriter",
+        instructions: "You are a space novel writer. Always research relevant facts and generate character profiles for the main characters before writing novels." +
+                      "Write complete chapters without asking for approval or feedback. Do not ask the user about tone, style, pace, or format preferences - just write the novel based on the request.",
+        tools: [AIFunctionFactory.Create(ResearchSpaceFactsAsync), AIFunctionFactory.Create(GenerateCharacterProfilesAsync)]);
+
+// Enable background responses (only supported by {Azure}OpenAI Responses at this time).
+AgentRunOptions options = new() { AllowBackgroundResponses = true };
+
+AgentSession session = await agent.CreateSessionAsync();
+
+// Start the initial run.
+AgentResponse response = await agent.RunAsync("Write a short story about a team of astronauts exploring an uncharted galaxy.", session, options);
+
+// Poll for background responses until complete.
+while (response.ContinuationToken is not null)
+{
+    await PersistAgentState(agent, session, response.ContinuationToken);
+
+    await Task.Delay(TimeSpan.FromSeconds(10));
+
+    var (restoredSession, continuationToken) = await RestoreAgentState(agent);
+
+    options.ContinuationToken = continuationToken;
+    response = await agent.RunAsync(restoredSession, options);
+}
+
+Console.WriteLine(response.Text);
+
+async Task PersistAgentState(AIAgent agent, AgentSession? session, ResponseContinuationToken? continuationToken)
+{
+    stateStore["session"] = await agent.SerializeSessionAsync(session!);
+    stateStore["continuationToken"] = JsonSerializer.SerializeToElement(continuationToken, AgentAbstractionsJsonUtilities.DefaultOptions.GetTypeInfo(typeof(ResponseContinuationToken)));
+}
+
+async Task<(AgentSession Session, ResponseContinuationToken? ContinuationToken)> RestoreAgentState(AIAgent agent)
+{
+    JsonElement serializedSession = stateStore["session"] ?? throw new InvalidOperationException("No serialized session found in state store.");
+    JsonElement? serializedToken = stateStore["continuationToken"];
+
+    AgentSession session = await agent.DeserializeSessionAsync(serializedSession);
+    ResponseContinuationToken? continuationToken = (ResponseContinuationToken?)serializedToken?.Deserialize(AgentAbstractionsJsonUtilities.DefaultOptions.GetTypeInfo(typeof(ResponseContinuationToken)));
+
+    return (session, continuationToken);
+}
+
+[Description("Researches relevant space facts and scientific information for writing a science fiction novel")]
+async Task<string> ResearchSpaceFactsAsync(string topic)
+{
+    Console.WriteLine($"[ResearchSpaceFacts] Researching topic: {topic}");
+
+    // Simulate a research operation
+    await Task.Delay(TimeSpan.FromSeconds(10));
+
+    string result = topic.ToUpperInvariant() switch
+    {
+        var t when t.Contains("GALAXY") => "Research findings: Galaxies contain billions of stars. Uncharted galaxies may have unique stellar formations, exotic matter, and unexplored phenomena like dark energy concentrations.",
+        var t when t.Contains("SPACE") || t.Contains("TRAVEL") => "Research findings: Interstellar travel requires advanced propulsion systems. Challenges include radiation exposure, life support, and navigation through unknown space.",
+        var t when t.Contains("ASTRONAUT") => "Research findings: Astronauts undergo rigorous training in zero-gravity environments, emergency protocols, spacecraft systems, and team dynamics for long-duration missions.",
+        _ => $"Research findings: General space exploration facts related to {topic}. Deep space missions require advanced technology, crew resilience, and contingency planning for unknown scenarios."
+    };
+
+    Console.WriteLine("[ResearchSpaceFacts] Research complete");
+    return result;
+}
+
+[Description("Generates character profiles for the main astronaut characters in the novel")]
+async Task<IEnumerable<string>> GenerateCharacterProfilesAsync()
+{
+    Console.WriteLine("[GenerateCharacterProfiles] Generating character profiles...");
+
+    // Simulate a character generation operation
+    await Task.Delay(TimeSpan.FromSeconds(10));
+
+    string[] profiles = [
+        "Captain Elena Voss: A seasoned mission commander with 15 years of experience. Strong-willed and decisive, she struggles with the weight of responsibility for her crew. Former military pilot turned astronaut.",
+            "Dr. James Chen: Chief science officer and astrophysicist. Brilliant but socially awkward, he finds solace in data and discovery. His curiosity often pushes the mission into uncharted territory.",
+            "Lieutenant Maya Torres: Navigation specialist and youngest crew member. Optimistic and tech-savvy, she brings fresh perspective and innovative problem-solving to challenges.",
+            "Commander Marcus Rivera: Chief engineer with expertise in spacecraft systems. Pragmatic and resourceful, he can fix almost anything with limited resources. Values crew safety above all.",
+            "Dr. Amara Okafor: Medical officer and psychologist. Empathetic and observant, she helps maintain crew morale and mental health during the long journey. Expert in space medicine."
+    ];
+
+    Console.WriteLine($"[GenerateCharacterProfiles] Generated {profiles.Length} character profiles");
+    return profiles;
+}

dotnet/samples/02-agents/Agents/Agent_Step10_BackgroundResponsesWithToolsAndPersistence/README.md

@@ -0,0 +1,28 @@
+# What This Sample Shows
+
+This sample demonstrates how to use background responses with ChatCompletionAgent and Azure OpenAI Responses for long-running operations. Background responses support:
+
+- **Polling for completion** - Non-streaming APIs can start a background operation and return a continuation token. Poll with the token until the response completes.
+- **Function calling** - Functions can be called during background operations.
+- **State persistence** - Thread and continuation token can be persisted and restored between polling cycles.
+
+> **Note:** Background responses are currently only supported by OpenAI Responses.
+
+For more information, see the [official documentation](https://learn.microsoft.com/en-us/agent-framework/user-guide/agents/agent-background-responses?pivots=programming-language-csharp).
+
+# Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure OpenAI service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
+```

dotnet/samples/02-agents/Agents/Agent_Step11_Middleware/Agent_Step11_Middleware.csproj

@@ -0,0 +1,21 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+    
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.Logging.Console" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+	  <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step11_Middleware/Program.cs

@@ -0,0 +1,328 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Middleware — Chain multiple middleware layers on an agent
+//
+// This sample shows multiple middleware layers working together with Azure AI Foundry:
+// chat client (global/per-request), agent run (PII filtering and guardrails),
+// function invocation (logging and result overrides), human-in-the-loop
+// approval workflows for sensitive function calls, and MessageAIContextProvider
+// middleware for injecting additional context messages into the agent pipeline.
+
+using System.ComponentModel;
+using System.Text.RegularExpressions;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+// Get Azure AI Foundry configuration from environment variables
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// Get a client to create/retrieve server side agents with
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+var aiProjectClient = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential());
+
+[Description("Get the weather for a given location.")]
+static string GetWeather([Description("The location to get the weather for.")] string location)
+    => $"The weather in {location} is cloudy with a high of 15°C.";
+
+[Description("The current datetime offset.")]
+static string GetDateTime()
+    => DateTimeOffset.Now.ToString();
+
+// Adding middleware to the chat client level and building an agent on top of it
+var originalAgent = aiProjectClient.AsAIAgent(
+    model: deploymentName,
+    instructions: "You are an AI assistant that helps people find information.",
+    tools: [AIFunctionFactory.Create(GetDateTime, name: nameof(GetDateTime))],
+    clientFactory: (chatClient) => chatClient
+        .AsBuilder()
+        .Use(getResponseFunc: ChatClientMiddleware, getStreamingResponseFunc: null)
+        .Build());
+
+// Adding middleware to the agent level
+var middlewareEnabledAgent = originalAgent
+    .AsBuilder()
+    .Use(FunctionCallMiddleware)
+    .Use(FunctionCallOverrideWeather)
+    .Use(PIIMiddleware, null)
+    .Use(GuardrailMiddleware, null)
+    .Build();
+
+var session = await middlewareEnabledAgent.CreateSessionAsync();
+
+Console.WriteLine("\n\n=== Example 1: Wording Guardrail ===");
+var guardRailedResponse = await middlewareEnabledAgent.RunAsync("Tell me something harmful.");
+Console.WriteLine($"Guard railed response: {guardRailedResponse}");
+
+Console.WriteLine("\n\n=== Example 2: PII detection ===");
+var piiResponse = await middlewareEnabledAgent.RunAsync("My name is John Doe, call me at 123-456-7890 or email me at john@something.com");
+Console.WriteLine($"Pii filtered response: {piiResponse}");
+
+Console.WriteLine("\n\n=== Example 3: Agent function middleware ===");
+
+// Agent function middleware support is limited to agents that wraps a upstream ChatClientAgent or derived from it.
+
+// Add Per-request tools
+var options = new ChatClientAgentRunOptions(new()
+{
+    Tools = [AIFunctionFactory.Create(GetWeather, name: nameof(GetWeather))]
+});
+
+var functionCallResponse = await middlewareEnabledAgent.RunAsync("What's the current time and the weather in Seattle?", session, options);
+Console.WriteLine($"Function calling response: {functionCallResponse}");
+
+// Special per-request middleware agent.
+Console.WriteLine("\n\n=== Example 4: Per-request middleware with human in the loop function approval ===");
+
+var optionsWithApproval = new ChatClientAgentRunOptions(new()
+{
+    // Adding a function with approval required
+    Tools = [new ApprovalRequiredAIFunction(AIFunctionFactory.Create(GetWeather, name: nameof(GetWeather)))],
+})
+{
+    ChatClientFactory = (chatClient) => chatClient
+        .AsBuilder()
+        .Use(PerRequestChatClientMiddleware, null) // Using the non-streaming for handling streaming as well
+        .Build()
+};
+
+// var response = middlewareAgent  // Using per-request middleware pipeline in addition to existing agent-level middleware
+var response = await originalAgent // Using per-request middleware pipeline without existing agent-level middleware
+    .AsBuilder()
+    .Use(PerRequestFunctionCallingMiddleware)
+    .Use(ConsolePromptingApprovalMiddleware, null)
+    .Build()
+    .RunAsync("What's the current time and the weather in Seattle?", session, optionsWithApproval);
+
+Console.WriteLine($"Per-request middleware response: {response}");
+
+// MessageAIContextProvider middleware that injects additional messages into the agent request.
+// This allows any AIAgent (not just ChatClientAgent) to benefit from MessageAIContextProvider-based
+// context enrichment. Multiple providers can be passed to Use and they are called in sequence,
+// each receiving the output of the previous one.
+Console.WriteLine("\n\n=== Example 5: MessageAIContextProvider middleware ===");
+
+var contextProviderAgent = originalAgent
+    .AsBuilder()
+    .UseAIContextProviders(new DateTimeContextProvider())
+    .Build();
+
+var contextResponse = await contextProviderAgent.RunAsync("Is it almost time for lunch?");
+Console.WriteLine($"Context-enriched response: {contextResponse}");
+
+// AIContextProvider at the chat client level. Unlike the agent-level MessageAIContextProvider,
+// this operates within the IChatClient pipeline and can also enrich tools and instructions.
+// It must be used within the context of a running AIAgent (uses AIAgent.CurrentRunContext).
+// In this case we are attaching an AIContextProvider that only adds messages.
+Console.WriteLine("\n\n=== Example 6: AIContextProvider on chat client pipeline ===");
+
+var chatClientProviderAgent = aiProjectClient.AsAIAgent(
+    model: deploymentName,
+    instructions: "You are an AI assistant that helps people find information.",
+    clientFactory: (chatClient) => chatClient
+        .AsBuilder()
+        .UseAIContextProviders(new DateTimeContextProvider())
+        .Build());
+
+var chatClientContextResponse = await chatClientProviderAgent.RunAsync("Is it almost time for lunch?");
+Console.WriteLine($"Chat client context-enriched response: {chatClientContextResponse}");
+
+// Function invocation middleware that logs before and after function calls.
+async ValueTask<object?> FunctionCallMiddleware(AIAgent agent, FunctionInvocationContext context, Func<FunctionInvocationContext, CancellationToken, ValueTask<object?>> next, CancellationToken cancellationToken)
+{
+    Console.WriteLine($"Function Name: {context!.Function.Name} - Middleware 1 Pre-Invoke");
+    var result = await next(context, cancellationToken);
+    Console.WriteLine($"Function Name: {context!.Function.Name} - Middleware 1 Post-Invoke");
+
+    return result;
+}
+
+// Function invocation middleware that overrides the result of the GetWeather function.
+async ValueTask<object?> FunctionCallOverrideWeather(AIAgent agent, FunctionInvocationContext context, Func<FunctionInvocationContext, CancellationToken, ValueTask<object?>> next, CancellationToken cancellationToken)
+{
+    Console.WriteLine($"Function Name: {context!.Function.Name} - Middleware 2 Pre-Invoke");
+
+    var result = await next(context, cancellationToken);
+
+    if (context.Function.Name == nameof(GetWeather))
+    {
+        // Override the result of the GetWeather function
+        result = "The weather is sunny with a high of 25°C.";
+    }
+    Console.WriteLine($"Function Name: {context!.Function.Name} - Middleware 2 Post-Invoke");
+    return result;
+}
+
+// There's no difference per-request middleware, except it's added to the agent and used for a single agent run.
+// This middleware logs function names before and after they are invoked.
+async ValueTask<object?> PerRequestFunctionCallingMiddleware(AIAgent agent, FunctionInvocationContext context, Func<FunctionInvocationContext, CancellationToken, ValueTask<object?>> next, CancellationToken cancellationToken)
+{
+    Console.WriteLine($"Agent Id: {agent.Id}");
+    Console.WriteLine($"Function Name: {context!.Function.Name} - Per-Request Pre-Invoke");
+    var result = await next(context, cancellationToken);
+    Console.WriteLine($"Function Name: {context!.Function.Name} - Per-Request Post-Invoke");
+    return result;
+}
+
+// This middleware redacts PII information from input and output messages.
+async Task<AgentResponse> PIIMiddleware(IEnumerable<ChatMessage> messages, AgentSession? session, AgentRunOptions? options, AIAgent innerAgent, CancellationToken cancellationToken)
+{
+    // Redact PII information from input messages
+    var filteredMessages = FilterMessages(messages);
+    Console.WriteLine("Pii Middleware - Filtered Messages Pre-Run");
+
+    var response = await innerAgent.RunAsync(filteredMessages, session, options, cancellationToken).ConfigureAwait(false);
+
+    // Redact PII information from output messages
+    response.Messages = FilterMessages(response.Messages);
+
+    Console.WriteLine("Pii Middleware - Filtered Messages Post-Run");
+
+    return response;
+
+    static IList<ChatMessage> FilterMessages(IEnumerable<ChatMessage> messages)
+    {
+        return messages.Select(m => new ChatMessage(m.Role, FilterPii(m.Text))).ToList();
+    }
+
+    static string FilterPii(string content)
+    {
+        // Regex patterns for PII detection (simplified for demonstration)
+        Regex[] piiPatterns =
+        [
+            MyRegex(), // Phone number (e.g., 123-456-7890)
+            EmailRegex(), // Email address
+            FullNameRegex() // Full name (e.g., John Doe)
+        ];
+
+        foreach (var pattern in piiPatterns)
+        {
+            content = pattern.Replace(content, "[REDACTED: PII]");
+        }
+
+        return content;
+    }
+}
+
+// This middleware enforces guardrails by redacting certain keywords from input and output messages.
+async Task<AgentResponse> GuardrailMiddleware(IEnumerable<ChatMessage> messages, AgentSession? session, AgentRunOptions? options, AIAgent innerAgent, CancellationToken cancellationToken)
+{
+    // Redact keywords from input messages
+    var filteredMessages = FilterMessages(messages);
+
+    Console.WriteLine("Guardrail Middleware - Filtered messages Pre-Run");
+
+    // Proceed with the agent run
+    var response = await innerAgent.RunAsync(filteredMessages, session, options, cancellationToken);
+
+    // Redact keywords from output messages
+    response.Messages = FilterMessages(response.Messages);
+
+    Console.WriteLine("Guardrail Middleware - Filtered messages Post-Run");
+
+    return response;
+
+    List<ChatMessage> FilterMessages(IEnumerable<ChatMessage> messages)
+    {
+        return messages.Select(m => new ChatMessage(m.Role, FilterContent(m.Text))).ToList();
+    }
+
+    static string FilterContent(string content)
+    {
+        foreach (var keyword in new[] { "harmful", "illegal", "violence" })
+        {
+            if (content.Contains(keyword, StringComparison.OrdinalIgnoreCase))
+            {
+                return "[REDACTED: Forbidden content]";
+            }
+        }
+
+        return content;
+    }
+}
+
+// This middleware handles Human in the loop console interaction for any user approval required during function calling.
+async Task<AgentResponse> ConsolePromptingApprovalMiddleware(IEnumerable<ChatMessage> messages, AgentSession? session, AgentRunOptions? options, AIAgent innerAgent, CancellationToken cancellationToken)
+{
+    AgentResponse response = await innerAgent.RunAsync(messages, session, options, cancellationToken);
+
+    // For simplicity, we are assuming here that only function approvals are pending.
+    List<ToolApprovalRequestContent> approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();
+
+    while (approvalRequests.Count > 0)
+    {
+        // Ask the user to approve each function call request.
+        // Pass the user input responses back to the agent for further processing.
+        response.Messages = approvalRequests
+            .ConvertAll(functionApprovalRequest =>
+            {
+                Console.WriteLine($"The agent would like to invoke the following function, please reply Y to approve: Name {((FunctionCallContent)functionApprovalRequest.ToolCall).Name}");
+                return new ChatMessage(ChatRole.User, [functionApprovalRequest.CreateResponse(Console.ReadLine()?.Equals("Y", StringComparison.OrdinalIgnoreCase) ?? false)]);
+            });
+
+        response = await innerAgent.RunAsync(response.Messages, session, options, cancellationToken);
+
+        approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();
+    }
+
+    return response;
+}
+
+// This middleware handles chat client lower level invocations.
+// This is useful for handling agent messages before they are sent to the LLM and also handle any response messages from the LLM before they are sent back to the agent.
+async Task<ChatResponse> ChatClientMiddleware(IEnumerable<ChatMessage> message, ChatOptions? options, IChatClient innerChatClient, CancellationToken cancellationToken)
+{
+    Console.WriteLine("Chat Client Middleware - Pre-Chat");
+    var response = await innerChatClient.GetResponseAsync(message, options, cancellationToken);
+    Console.WriteLine("Chat Client Middleware - Post-Chat");
+
+    return response;
+}
+
+// There's no difference per-request middleware, except it's added to the chat client and used for a single agent run.
+// This middleware handles chat client lower level invocations.
+// This is useful for handling agent messages before they are sent to the LLM and also handle any response messages from the LLM before they are sent back to the agent.
+async Task<ChatResponse> PerRequestChatClientMiddleware(IEnumerable<ChatMessage> message, ChatOptions? options, IChatClient innerChatClient, CancellationToken cancellationToken)
+{
+    Console.WriteLine("Per-Request Chat Client Middleware - Pre-Chat");
+    var response = await innerChatClient.GetResponseAsync(message, options, cancellationToken);
+    Console.WriteLine("Per-Request Chat Client Middleware - Post-Chat");
+
+    return response;
+}
+
+/// <summary>
+/// A <see cref="MessageAIContextProvider"/> that injects the current date and time into the agent's context.
+/// This is a simple example of how to use a MessageAIContextProvider to enrich agent messages
+/// via the <see cref="AIAgentBuilder.UseAIContextProviders(MessageAIContextProvider[])"/> extension method.
+/// </summary>
+internal sealed class DateTimeContextProvider : MessageAIContextProvider
+{
+    protected override ValueTask<IEnumerable<ChatMessage>> ProvideMessagesAsync(
+        InvokingContext context,
+        CancellationToken cancellationToken = default)
+    {
+        Console.WriteLine("DateTimeContextProvider - Injecting current date/time context");
+
+        return new ValueTask<IEnumerable<ChatMessage>>(
+            [
+                new ChatMessage(ChatRole.User, $"For reference, the current date and time is: {DateTimeOffset.Now}")
+            ]);
+    }
+}
+
+internal partial class Program
+{
+    [GeneratedRegex(@"\b\d{3}-\d{3}-\d{4}\b", RegexOptions.Compiled)]
+    private static partial Regex MyRegex();
+
+    [GeneratedRegex(@"\b[\w\.-]+@[\w\.-]+\.\w+\b", RegexOptions.Compiled)]
+    private static partial Regex EmailRegex();
+
+    [GeneratedRegex(@"\b[A-Z][a-z]+\s[A-Z][a-z]+\b", RegexOptions.Compiled)]
+    private static partial Regex FullNameRegex();
+}

dotnet/samples/02-agents/Agents/Agent_Step11_Middleware/README.md

@@ -0,0 +1,42 @@
+# Agent Middleware 
+
+This sample demonstrates how to add middleware to intercept:
+- Chat client calls (global and per‑request)
+- Agent runs (guardrails and PII filtering)
+- Function calling (logging/override)
+
+## What This Sample Shows
+
+1. Microsoft Foundry integration via `AIProjectClient` and `DefaultAzureCredential`
+2. Chat client middleware using `ChatClientBuilder.Use(...)`
+3. Agent run middleware (PII redaction and wording guardrails)
+4. Function invocation middleware (logging and overriding a tool result)
+5. Per‑request chat client middleware
+6. Per‑request function pipeline with approval
+7. Combining agent‑level and per‑request middleware
+8. MessageAIContextProvider middleware via `AIAgentBuilder.Use(...)` for injecting additional context messages
+9. AIContextProvider middleware via `ChatClientBuilder.Use(...)` for enriching messages, tools, and instructions at the chat client level
+
+## Function Invocation Middleware
+
+Not all agents support function invocation middleware.
+
+Attempting to use function middleware on agents that do not wrap a ChatClientAgent or derives from it will throw an InvalidOperationException.
+
+## Prerequisites
+
+1. Environment variables:
+   - `FOUNDRY_PROJECT_ENDPOINT`: Your Foundry project endpoint
+   - `FOUNDRY_MODEL`: Model name (optional; defaults to `gpt-5.4-mini`)
+2. Sign in with Azure CLI (PowerShell):
+   ```powershell
+   az login
+   ```
+
+## Running the Sample
+
+Use PowerShell:
+```powershell
+cd dotnet/samples/02-agents/Agents/Agent_Step11_Middleware
+dotnet run
+```

dotnet/samples/02-agents/Agents/Agent_Step12_Plugins/Agent_Step12_Plugins.csproj

@@ -0,0 +1,23 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <NoWarn>$(NoWarn);CA1812</NoWarn>
+    <RootNamespace>Agent_Step12_Plugins</RootNamespace>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.Logging.Console" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
+  </ItemGroup>
+  
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step12_Plugins/Program.cs

@@ -0,0 +1,134 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Plugins — Use plugin classes with dependency injection
+//
+// This sample shows how to use plugins with an AI agent. Plugin classes can
+// depend on other services that need to be injected. In this sample, the
+// AgentPlugin class uses the WeatherProvider and CurrentTimeProvider classes
+// to get weather and current time information. Both services are registered
+// in the service collection and injected into the plugin.
+// Plugin classes may have many methods, but only some are intended to be used
+// as AI functions. The AsAITools method of the plugin class shows how to specify
+// which methods should be exposed to the AI agent.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.DependencyInjection;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// Create a service collection to hold the agent plugin and its dependencies.
+ServiceCollection services = new();
+services.AddSingleton<WeatherProvider>();
+services.AddSingleton<CurrentTimeProvider>();
+services.AddSingleton<AgentPlugin>(); // The plugin depends on WeatherProvider and CurrentTimeProvider registered above.
+
+IServiceProvider serviceProvider = services.BuildServiceProvider();
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AIProjectClient(
+    new Uri(endpoint),
+    new DefaultAzureCredential())
+    .AsAIAgent(
+        model: deploymentName,
+        instructions: "You are a helpful assistant that helps people find information.",
+        name: "Assistant",
+        tools: [.. serviceProvider.GetRequiredService<AgentPlugin>().AsAITools()],
+        services: serviceProvider); // Pass the service provider to the agent so it will be available to plugin functions to resolve dependencies.
+
+Console.WriteLine(await agent.RunAsync("Tell me current time and weather in Seattle."));
+
+/// <summary>
+/// The agent plugin that provides weather and current time information.
+/// </summary>
+/// <param name="weatherProvider">The weather provider to get weather information.</param>
+internal sealed class AgentPlugin(WeatherProvider weatherProvider)
+{
+    /// <summary>
+    /// Gets the weather information for the specified location.
+    /// </summary>
+    /// <remarks>
+    /// This method demonstrates how to use the dependency that was injected into the plugin class.
+    /// </remarks>
+    /// <param name="location">The location to get the weather for.</param>
+    /// <returns>The weather information for the specified location.</returns>
+    public string GetWeather(string location)
+    {
+        return weatherProvider.GetWeather(location);
+    }
+
+    /// <summary>
+    /// Gets the current date and time for the specified location.
+    /// </summary>
+    /// <remarks>
+    /// This method demonstrates how to resolve a dependency using the service provider passed to the method.
+    /// </remarks>
+    /// <param name="sp">The service provider to resolve the <see cref="CurrentTimeProvider"/>.</param>
+    /// <param name="location">The location to get the current time for.</param>
+    /// <returns>The current date and time as a <see cref="DateTimeOffset"/>.</returns>
+    public DateTimeOffset GetCurrentTime(IServiceProvider sp, string location)
+    {
+        // Resolve the CurrentTimeProvider from the service provider
+        var currentTimeProvider = sp.GetRequiredService<CurrentTimeProvider>();
+
+        return currentTimeProvider.GetCurrentTime(location);
+    }
+
+    /// <summary>
+    /// Returns the functions provided by this plugin.
+    /// </summary>
+    /// <remarks>
+    /// In real world scenarios, a class may have many methods and only a subset of them may be intended to be exposed as AI functions.
+    /// This method demonstrates how to explicitly specify which methods should be exposed to the AI agent.
+    /// </remarks>
+    /// <returns>The functions provided by this plugin.</returns>
+    public IEnumerable<AITool> AsAITools()
+    {
+        yield return AIFunctionFactory.Create(this.GetWeather);
+        yield return AIFunctionFactory.Create(this.GetCurrentTime);
+    }
+}
+
+/// <summary>
+/// The weather provider that returns weather information.
+/// </summary>
+internal sealed class WeatherProvider
+{
+    /// <summary>
+    /// Gets the weather information for the specified location.
+    /// </summary>
+    /// <remarks>
+    /// The weather information is hardcoded for demonstration purposes.
+    /// In a real application, this could call a weather API to get actual weather data.
+    /// </remarks>
+    /// <param name="location">The location to get the weather for.</param>
+    /// <returns>The weather information for the specified location.</returns>
+    public string GetWeather(string location)
+    {
+        return $"The weather in {location} is cloudy with a high of 15°C.";
+    }
+}
+
+/// <summary>
+/// Provides the current date and time.
+/// </summary>
+/// <remarks>
+/// This class returns the current date and time using the system's clock.
+/// </remarks>
+internal sealed class CurrentTimeProvider
+{
+    /// <summary>
+    /// Gets the current date and time.
+    /// </summary>
+    /// <param name="location">The location to get the current time for (not used in this implementation).</param>
+    /// <returns>The current date and time as a <see cref="DateTimeOffset"/>.</returns>
+    public DateTimeOffset GetCurrentTime(string location)
+    {
+        return DateTimeOffset.Now;
+    }
+}

dotnet/samples/02-agents/Agents/Agent_Step13_ChatReduction/Agent_Step13_ChatReduction.csproj

@@ -0,0 +1,16 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step13_ChatReduction/Program.cs

@@ -0,0 +1,79 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Chat Reduction — Keep conversation context within model limits
+//
+// This sample shows how to use a chat history reducer to keep the context
+// within model size limits. Any IChatReducer implementation can customize
+// how the chat history is reduced.
+// NOTE: This feature is only supported where chat history is stored locally
+// (e.g. OpenAI Chat Completion). For server-side history (e.g. Foundry Agents),
+// the service manages chat history size.
+
+using Azure.AI.Extensions.OpenAI;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// Construct the agent, and provide a factory to create an in-memory chat message store with a reducer that keeps only the last 2 non-system messages.
+// You must dissable client side conversation storage for clients that support it.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AIProjectClient(
+    new Uri(endpoint),
+    new DefaultAzureCredential())
+    .GetProjectOpenAIClient()
+    .GetProjectResponsesClient()
+    .AsIChatClientWithStoredOutputDisabled(deploymentName)
+    .AsAIAgent(new ChatClientAgentOptions
+    {
+        ChatOptions = new() { ModelId = deploymentName, Instructions = "You are good at telling jokes." },
+        Name = "Joker",
+        ChatHistoryProvider = new InMemoryChatHistoryProvider(new() { ChatReducer = new MessageCountingChatReducer(2) })
+    });
+
+AgentSession session = await agent.CreateSessionAsync();
+
+// Invoke the agent and output the text result.
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a pirate.", session));
+
+// Get the chat history to see how many messages are stored.
+// We can use the ChatHistoryProvider, that is also used by the agent, to read the
+// chat history from the session state, and see how the reducer is affecting the stored messages.
+// Here we expect to see 2 messages, the original user message and the agent response message.
+if (session.TryGetInMemoryChatHistory(out var chatHistory))
+{
+    Console.WriteLine($"\nChat history has {chatHistory.Count} messages.\n");
+}
+
+// Invoke the agent a few more times.
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a robot.", session));
+
+// Now we expect to see 4 messages in the chat history, 2 input and 2 output.
+// While the target number of messages is 2, the default time for the InMemoryChatHistoryProvider
+// to trigger the reducer is just before messages are contributed to a new agent run.
+// So at this time, we have not yet triggered the reducer for the most recently added messages,
+// and they are still in the chat history.
+if (session.TryGetInMemoryChatHistory(out chatHistory))
+{
+    Console.WriteLine($"\nChat history has {chatHistory.Count} messages.\n");
+}
+
+Console.WriteLine(await agent.RunAsync("Tell me a joke about a lemur.", session));
+if (session.TryGetInMemoryChatHistory(out chatHistory))
+{
+    Console.WriteLine($"\nChat history has {chatHistory.Count} messages.\n");
+}
+
+// At this point, the chat history has exceeded the limit and the original message will not exist anymore,
+// so asking a follow up question about it may not work as expected.
+Console.WriteLine(await agent.RunAsync("What was the first joke I asked you to tell again?", session));
+
+if (session.TryGetInMemoryChatHistory(out chatHistory))
+{
+    Console.WriteLine($"\nChat history has {chatHistory.Count} messages.\n");
+}

dotnet/samples/02-agents/Agents/Agent_Step14_BackgroundResponses/Agent_Step14_BackgroundResponses.csproj

@@ -0,0 +1,16 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+  
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step14_BackgroundResponses/Program.cs

@@ -0,0 +1,79 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Background Responses — Asynchronous agent execution with polling
+//
+// This sample shows how to use background responses with ChatClientAgent
+// and Azure AI Foundry for non-blocking agent execution.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AIProjectClient(
+    new Uri(endpoint),
+    new DefaultAzureCredential())
+     .AsAIAgent(model: deploymentName, instructions: "You are a helpful assistant.");
+
+// Enable background responses (only supported by OpenAI Responses at this time).
+AgentRunOptions options = new() { AllowBackgroundResponses = true };
+
+AgentSession session = await agent.CreateSessionAsync();
+
+// Start the initial run.
+AgentResponse response = await agent.RunAsync("Write a very long novel about otters in space.", session, options);
+
+// Poll until the response is complete.
+while (response.ContinuationToken is { } token)
+{
+    // Wait before polling again.
+    await Task.Delay(TimeSpan.FromSeconds(2));
+
+    // Continue with the token.
+    options.ContinuationToken = token;
+
+    response = await agent.RunAsync(session, options);
+}
+
+// Display the result.
+Console.WriteLine(response.Text);
+
+// Reset options and session for streaming.
+options = new() { AllowBackgroundResponses = true };
+session = await agent.CreateSessionAsync();
+
+AgentResponseUpdate? lastReceivedUpdate = null;
+// Start streaming.
+await foreach (AgentResponseUpdate update in agent.RunStreamingAsync("Write a very long novel about otters in space.", session, options))
+{
+    // Output each update.
+    Console.Write(update.Text);
+
+    // Track the last update that carries a resumable continuation token.
+    // Lifecycle events like response.completed return null tokens (response is finished),
+    // so we only update our reference when a token is actually present.
+    if (update.ContinuationToken is not null)
+    {
+        lastReceivedUpdate = update;
+    }
+
+    // Simulate connection loss after first piece of content received.
+    if (update.Text.Length > 0)
+    {
+        break;
+    }
+}
+
+// Resume from interruption point.
+options.ContinuationToken = lastReceivedUpdate?.ContinuationToken;
+
+await foreach (AgentResponseUpdate update in agent.RunStreamingAsync(session, options))
+{
+    // Output each update.
+    Console.Write(update.Text);
+}

dotnet/samples/02-agents/Agents/Agent_Step14_BackgroundResponses/README.md

@@ -0,0 +1,27 @@
+# What This Sample Shows
+
+This sample demonstrates how to use background responses with ChatCompletionAgent and Azure OpenAI Responses for long-running operations. Background responses support:
+
+- **Polling for completion** - Non-streaming APIs can start a background operation and return a continuation token. Poll with the token until the response completes.
+- **Resuming after interruption** - Streaming APIs can be interrupted and resumed from the last update using the continuation token.
+
+> **Note:** Background responses are currently only supported by OpenAI Responses.
+
+For more information, see the [official documentation](https://learn.microsoft.com/en-us/agent-framework/user-guide/agents/agent-background-responses?pivots=programming-language-csharp).
+
+# Prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Azure OpenAI service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+
+**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure OpenAI resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" # Replace with your Azure OpenAI resource endpoint
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
+```

dotnet/samples/02-agents/Agents/Agent_Step15_DeepResearch/Agent_Step15_DeepResearch.csproj

@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.Agents.Persistent" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI.Persistent\Microsoft.Agents.AI.AzureAI.Persistent.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step15_DeepResearch/Program.cs

@@ -0,0 +1,57 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+#pragma warning disable CS0618 // Type or member is obsolete - sample uses deprecated PersistentAgentsClientExtensions
+
+// This sample shows how to create a Microsoft Foundry Agent with the Deep Research Tool.
+
+using Azure.AI.Agents.Persistent;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deepResearchDeploymentName = Environment.GetEnvironmentVariable("AZURE_AI_REASONING_DEPLOYMENT_NAME") ?? "o3-deep-research";
+var modelDeploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+var bingConnectionId = Environment.GetEnvironmentVariable("AZURE_AI_BING_CONNECTION_ID") ?? throw new InvalidOperationException("AZURE_AI_BING_CONNECTION_ID is not set.");
+
+// Configure extended network timeout for long-running Deep Research tasks.
+PersistentAgentsAdministrationClientOptions persistentAgentsClientOptions = new();
+persistentAgentsClientOptions.Retry.NetworkTimeout = TimeSpan.FromMinutes(20);
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+// Get a client to create/retrieve server side agents with.
+PersistentAgentsClient persistentAgentsClient = new(endpoint, new DefaultAzureCredential(), persistentAgentsClientOptions);
+
+// Define and configure the Deep Research tool.
+DeepResearchToolDefinition deepResearchTool = new(new DeepResearchDetails(
+    bingGroundingConnections: [new(bingConnectionId)],
+    model: deepResearchDeploymentName)
+ );
+
+// Create an agent with the Deep Research tool on the Azure AI agent service.
+AIAgent agent = await persistentAgentsClient.CreateAIAgentAsync(
+    model: modelDeploymentName,
+    name: "DeepResearchAgent",
+    instructions: "You are a helpful Agent that assists in researching scientific topics.",
+    tools: [deepResearchTool]);
+
+const string Task = "Research the current state of studies on orca intelligence and orca language, " +
+    "including what is currently known about orcas' cognitive capabilities and communication systems.";
+
+Console.WriteLine($"# User: '{Task}'");
+Console.WriteLine();
+
+try
+{
+    AgentSession session = await agent.CreateSessionAsync();
+
+    await foreach (var response in agent.RunStreamingAsync(Task, session))
+    {
+        Console.Write(response.Text);
+    }
+}
+finally
+{
+    await persistentAgentsClient.Administration.DeleteAgentAsync(agent.Id);
+}

dotnet/samples/02-agents/Agents/Agent_Step15_DeepResearch/README.md

@@ -0,0 +1,49 @@
+# What this sample demonstrates
+
+This sample demonstrates how to create an Azure AI Agent with the Deep Research Tool, which leverages the o3-deep-research reasoning model to perform comprehensive research on complex topics.
+
+Key features:
+- Configuring and using the Deep Research Tool with Bing grounding
+- Creating a persistent AI agent with deep research capabilities
+- Executing deep research queries and retrieving results
+
+## Prerequisites
+
+Before running this sample, ensure you have:
+
+1. A Microsoft Foundry project set up
+2. A deep research model deployment (e.g., o3-deep-research)
+3. A model deployment (e.g., gpt-5.4-mini)
+4. A Bing Connection configured in your Microsoft Foundry project
+5. Azure CLI installed and authenticated
+
+**Important**: Please visit the following documentation for detailed setup instructions:
+- [Deep Research Tool Documentation](https://aka.ms/agents-deep-research)
+- [Research Tool Setup](https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/deep-research#research-tool-setup)
+
+Pay special attention to the purple `Note` boxes in the Azure documentation.
+
+**Note**: The Bing Grounding Connection ID must be the **full ARM resource URI** from the project, not just the connection name. It has the following format:
+
+```
+/subscriptions/<sub-id>/resourceGroups/<rg>/providers/Microsoft.CognitiveServices/accounts/<account>/projects/<project>/connections/<connection-name>
+```
+
+You can find this in the Microsoft Foundry portal under **Management > Connected resources**, or retrieve it programmatically via the connections API (`.id` property).
+
+## Environment Variables
+
+Set the following environment variables:
+
+```powershell
+# Replace with your Microsoft Foundry project endpoint
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/"
+
+# Replace with your Bing Grounding connection ID (full ARM resource URI)
+$env:AZURE_AI_BING_CONNECTION_ID="/subscriptions/<sub-id>/resourceGroups/<rg>/providers/Microsoft.CognitiveServices/accounts/<account>/projects/<project>/connections/<connection-name>"
+
+# Optional, defaults to o3-deep-research
+$env:AZURE_AI_REASONING_DEPLOYMENT_NAME="o3-deep-research"
+
+# Optional, defaults to gpt-5.4-mini
+$env:FOUNDRY_MODEL="gpt-5.4-mini"

dotnet/samples/02-agents/Agents/Agent_Step16_Declarative/Agent_Step16_Declarative.csproj

@@ -0,0 +1,23 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Agents.ObjectModel" />
+    <PackageReference Include="Microsoft.Agents.ObjectModel.Json" />
+    <PackageReference Include="Microsoft.Agents.ObjectModel.PowerFx" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Declarative\Microsoft.Agents.AI.Declarative.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step16_Declarative/Program.cs

@@ -0,0 +1,57 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Declarative Agent — Create an agent from a YAML definition
+//
+// This sample shows how to create an agent from a YAML-based
+// declarative representation.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// Create the chat client
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+IChatClient chatClient = aiProjectClient.GetProjectOpenAIClient().GetResponsesClient().AsIChatClient(deploymentName);
+
+// Define the agent using a YAML definition.
+var text =
+    """
+    kind: Prompt
+    name: Assistant
+    description: Helpful assistant
+    instructions: You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format.
+    model:
+        options:
+            temperature: 0.9
+            topP: 0.95
+    outputSchema:
+        properties:
+            language:
+                type: string
+                required: true
+                description: The language of the answer.
+            answer:
+                type: string
+                required: true
+                description: The answer text.
+    """;
+
+// Create the agent from the YAML definition.
+var agentFactory = new ChatClientPromptAgentFactory(chatClient);
+var agent = await agentFactory.CreateFromYamlAsync(text);
+
+// Invoke the agent and output the text result.
+Console.WriteLine(await agent!.RunAsync("Tell me a joke about a pirate in English."));
+
+// Invoke the agent with streaming support.
+await foreach (var update in agent!.RunStreamingAsync("Tell me a joke about a pirate in French."))
+{
+    Console.WriteLine(update);
+}

dotnet/samples/02-agents/Agents/Agent_Step17_AdditionalAIContext/Agent_Step17_AdditionalAIContext.csproj

@@ -0,0 +1,16 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step17_AdditionalAIContext/Program.cs

@@ -0,0 +1,172 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Additional AI Context — Inject context via custom AIContextProviders
+//
+// This sample shows how to inject additional AI context into a ChatClientAgent
+// using custom AIContextProvider components. Multiple providers can be attached
+// and are called in sequence, each receiving accumulated context from the previous.
+// This mechanism is useful for injecting RAG results, memories, or other context.
+// Agent Framework also provides built-in AIContextProviders for many scenarios.
+
+#pragma warning disable CA1869 // Cache and reuse 'JsonSerializerOptions' instances
+
+using System.Text;
+using System.Text.Json;
+using Azure.AI.Extensions.OpenAI;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using SampleApp;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// A sample function to load the next three calendar events for the user.
+Func<Task<string[]>> loadNextThreeCalendarEvents = async () =>
+{
+    // In a real implementation, this method would connect to a calendar service
+    return
+    [
+        "Doctor's appointment today at 15:00",
+        "Team meeting today at 17:00",
+        "Birthday party today at 20:00"
+    ];
+};
+
+// Create an agent with an AI context provider attached that aggregates two other providers.
+// You must dissable client side conversation storage for clients that support it:
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AIProjectClient(
+    new Uri(endpoint),
+    new DefaultAzureCredential())
+    .GetProjectOpenAIClient()
+    .GetProjectResponsesClient()
+    .AsIChatClientWithStoredOutputDisabled(deploymentName)
+    .AsAIAgent(new ChatClientAgentOptions()
+    {
+        ChatOptions = new() { ModelId = deploymentName, Instructions = """
+        You are a helpful personal assistant.
+        You manage a TODO list for the user. When the user has completed one of the tasks it can be removed from the TODO list. Only provide the list of TODO items if asked.
+        You remind users of upcoming calendar events when the user interacts with you.
+        """ },
+        ChatHistoryProvider = new InMemoryChatHistoryProvider(new InMemoryChatHistoryProviderOptions
+        {
+            // Use StorageInputRequestMessageFilter to provide a custom filter for request messages stored in chat history.
+            // By default the chat history provider will store all messages, except for those that came from chat history in the first place.
+            // In this case, we want to also exclude messages that came from AI context providers.
+            // You may want to store these messages, depending on their content and your requirements.
+            StorageInputRequestMessageFilter = messages => messages.Where(m => m.GetAgentRequestMessageSourceType() != AgentRequestMessageSourceType.AIContextProvider && m.GetAgentRequestMessageSourceType() != AgentRequestMessageSourceType.ChatHistory)
+        }),
+        // Add multiple AI context providers: one that maintains a todo list and one that provides upcoming calendar entries.
+        // The agent will call each provider in sequence, accumulating context from each.
+        AIContextProviders = [
+            new TodoListAIContextProvider(),
+            new CalendarSearchAIContextProvider(loadNextThreeCalendarEvents)
+        ],
+    });
+
+// Invoke the agent and output the text result.
+AgentSession session = await agent.CreateSessionAsync();
+Console.WriteLine(await agent.RunAsync("I need to pick up milk from the supermarket.", session) + "\n");
+Console.WriteLine(await agent.RunAsync("I need to take Sally for soccer practice.", session) + "\n");
+Console.WriteLine(await agent.RunAsync("I need to make a dentist appointment for Jimmy.", session) + "\n");
+Console.WriteLine(await agent.RunAsync("I've taken Sally to soccer practice.", session) + "\n");
+
+// We can serialize the session, and it will contain both the chat history and the data that each AI context provider serialized.
+JsonElement serializedSession = await agent.SerializeSessionAsync(session);
+// Let's print it to console to show the contents.
+Console.WriteLine(JsonSerializer.Serialize(serializedSession, options: new JsonSerializerOptions() { WriteIndented = true, IndentSize = 2 }) + "\n");
+// The serialized session can be stored long term in a persistent store, but in this case we will just deserialize again and continue the conversation.
+session = await agent.DeserializeSessionAsync(serializedSession);
+
+Console.WriteLine(await agent.RunAsync("Considering my appointments, can you create a plan for my day that plans out when I should complete the items on my todo list?", session) + "\n");
+
+namespace SampleApp
+{
+    /// <summary>
+    /// An <see cref="AIContextProvider"/>, which maintains a todo list for the agent.
+    /// </summary>
+    internal sealed class TodoListAIContextProvider : AIContextProvider
+    {
+        private static List<string> GetTodoItems(AgentSession? session)
+            => session?.StateBag.GetValue<List<string>>(nameof(TodoListAIContextProvider)) ?? [];
+
+        private static void SetTodoItems(AgentSession? session, List<string> items)
+            => session?.StateBag.SetValue(nameof(TodoListAIContextProvider), items);
+
+        protected override ValueTask<AIContext> ProvideAIContextAsync(InvokingContext context, CancellationToken cancellationToken = default)
+        {
+            var todoItems = GetTodoItems(context.Session);
+
+            StringBuilder outputMessageBuilder = new();
+            outputMessageBuilder.AppendLine("Your todo list contains the following items:");
+
+            if (todoItems.Count == 0)
+            {
+                outputMessageBuilder.AppendLine("  (no items)");
+            }
+            else
+            {
+                for (int i = 0; i < todoItems.Count; i++)
+                {
+                    outputMessageBuilder.AppendLine($"{i}. {todoItems[i]}");
+                }
+            }
+
+            return new ValueTask<AIContext>(new AIContext
+            {
+                Tools =
+                [
+                    AIFunctionFactory.Create((string item) => AddTodoItem(context.Session, item), "AddTodoItem", "Adds an item to the todo list."),
+                    AIFunctionFactory.Create((int index) => RemoveTodoItem(context.Session, index), "RemoveTodoItem", "Removes an item from the todo list. Index is zero based.")
+                ],
+                Messages =
+                [
+                new ChatMessage(ChatRole.User, outputMessageBuilder.ToString())
+                ]
+            });
+        }
+
+        private static void RemoveTodoItem(AgentSession? session, int index)
+        {
+            var items = GetTodoItems(session);
+            items.RemoveAt(index);
+            SetTodoItems(session, items);
+        }
+
+        private static void AddTodoItem(AgentSession? session, string item)
+        {
+            if (string.IsNullOrWhiteSpace(item))
+            {
+                throw new ArgumentException("Item must have a value");
+            }
+
+            var items = GetTodoItems(session);
+            items.Add(item);
+            SetTodoItems(session, items);
+        }
+    }
+
+    /// <summary>
+    /// A <see cref="MessageAIContextProvider"/> which searches for upcoming calendar events and adds them to the AI context.
+    /// </summary>
+    internal sealed class CalendarSearchAIContextProvider(Func<Task<string[]>> loadNextThreeCalendarEvents) : MessageAIContextProvider
+    {
+        protected override async ValueTask<IEnumerable<ChatMessage>> ProvideMessagesAsync(InvokingContext context, CancellationToken cancellationToken = default)
+        {
+            var events = await loadNextThreeCalendarEvents();
+
+            StringBuilder outputMessageBuilder = new();
+            outputMessageBuilder.AppendLine("You have the following upcoming calendar events:");
+            foreach (var calendarEvent in events)
+            {
+                outputMessageBuilder.AppendLine($" - {calendarEvent}");
+            }
+
+            return [new ChatMessage(ChatRole.User, outputMessageBuilder.ToString())];
+        }
+    }
+}

dotnet/samples/02-agents/Agents/Agent_Step18_CompactionPipeline/Agent_Step18_CompactionPipeline.csproj

@@ -0,0 +1,16 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step18_CompactionPipeline/Program.cs

@@ -0,0 +1,123 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Compaction Pipeline — Progressive context management strategies
+//
+// This sample demonstrates how to use a CompactionProvider with a compaction
+// pipeline as an AIContextProvider for in-run context management. The pipeline
+// chains multiple compaction strategies from gentle to aggressive:
+//   1. ToolResultCompactionStrategy — Collapses old tool-call groups into summaries
+//   2. SummarizationCompactionStrategy — LLM-compresses older conversation spans
+//   3. SlidingWindowCompactionStrategy — Keeps only the most recent N user turns
+//   4. TruncationCompactionStrategy — Emergency token-budget backstop
+
+using System.ComponentModel;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Compaction;
+using Microsoft.Extensions.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Create a chat client for the agent and a separate one for the summarization strategy.
+// Using the same model for simplicity; in production, use a smaller/cheaper model for summarization.
+IChatClient agentChatClient = aiProjectClient.GetProjectOpenAIClient().GetResponsesClient().AsIChatClient(deploymentName);
+IChatClient summarizerChatClient = aiProjectClient.GetProjectOpenAIClient().GetResponsesClient().AsIChatClient(deploymentName);
+
+// Define a tool the agent can use, so we can see tool-result compaction in action.
+[Description("Look up the current price of a product by name.")]
+static string LookupPrice([Description("The product name to look up.")] string productName) =>
+    productName.ToUpperInvariant() switch
+    {
+        "LAPTOP" => "The laptop costs $999.99.",
+        "KEYBOARD" => "The keyboard costs $79.99.",
+        "MOUSE" => "The mouse costs $29.99.",
+        _ => $"Sorry, I don't have pricing for '{productName}'."
+    };
+
+// Configure the compaction pipeline with one of each strategy, ordered least to most aggressive.
+PipelineCompactionStrategy compactionPipeline =
+    new(// 1. Gentle: collapse old tool-call groups into short summaries
+        new ToolResultCompactionStrategy(CompactionTriggers.MessagesExceed(7)),
+
+        // 2. Moderate: use an LLM to summarize older conversation spans into a concise message
+        new SummarizationCompactionStrategy(summarizerChatClient, CompactionTriggers.TokensExceed(0x500)),
+
+        // 3. Aggressive: keep only the last N user turns and their responses
+        new SlidingWindowCompactionStrategy(CompactionTriggers.TurnsExceed(4)),
+
+        // 4. Emergency: drop oldest groups until under the token budget
+        new TruncationCompactionStrategy(CompactionTriggers.TokensExceed(0x8000)));
+
+// Create the agent with a CompactionProvider that uses the compaction pipeline.
+AIAgent agent =
+    agentChatClient
+        .AsBuilder()
+        // Note: Adding the CompactionProvider at the builder level means it will be applied to all agents
+        // built from this builder and will manage context for both agent messages and tool calls.
+        .UseAIContextProviders(new CompactionProvider(compactionPipeline))
+        .BuildAIAgent(
+            new ChatClientAgentOptions
+            {
+                Name = "ShoppingAssistant",
+                ChatOptions = new()
+                {
+                    Instructions =
+                        """
+                        You are a helpful, but long winded, shopping assistant.
+                        Help the user look up prices and compare products.
+                        You MUST use the LookupPrice tool for every price question — never answer price questions from memory.
+                        When responding, Be sure to be extra descriptive and use as
+                        many words as possible without sounding ridiculous.
+                        """,
+                    Tools = [AIFunctionFactory.Create(LookupPrice)]
+                },
+                // Note: AIContextProviders may be specified here instead of ChatClientBuilder.UseAIContextProviders.
+                // Specifying compaction at the agent level skips compaction in the function calling loop.
+                //AIContextProviders = [new CompactionProvider(compactionPipeline)]
+            });
+
+AgentSession session = await agent.CreateSessionAsync();
+
+// Helper to print chat history size
+void PrintChatHistory()
+{
+    if (session.TryGetInMemoryChatHistory(out var history))
+    {
+        Console.ForegroundColor = ConsoleColor.Cyan;
+        Console.WriteLine($"\n[Messages: #{history.Count}]\n");
+        Console.ResetColor();
+    }
+}
+
+// Run a multi-turn conversation with tool calls to exercise the pipeline.
+string[] prompts =
+[
+    "What's the price of a laptop?",
+    "How about a keyboard?",
+    "And a mouse?",
+    "Which product is the cheapest?",
+    "Can you compare the laptop and the keyboard for me?",
+    "What was the first product I asked about?",
+    "Thank you!",
+];
+
+foreach (string prompt in prompts)
+{
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[User] ");
+    Console.ResetColor();
+    Console.WriteLine(prompt);
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+    Console.WriteLine(await agent.RunAsync(prompt, session));
+
+    PrintChatHistory();
+}

dotnet/samples/02-agents/Agents/Agent_Step18_CompactionPipeline/README.md

@@ -0,0 +1,142 @@
+# Compaction Pipeline
+
+This sample demonstrates how to use a `CompactionProvider` with a `PipelineCompactionStrategy` to manage long conversation histories in a token-efficient way. The pipeline chains four compaction strategies, ordered from gentle to aggressive, so that the least disruptive strategy runs first and more aggressive strategies only activate when necessary.
+
+## What This Sample Shows
+
+- **`CompactionProvider`** — an `AIContextProvider` that applies a compaction strategy before each agent invocation, keeping only the most relevant messages within the model's context window
+- **`PipelineCompactionStrategy`** — chains multiple compaction strategies into an ordered pipeline; each strategy evaluates its own trigger independently and operates on the output of the previous one
+- **`ToolResultCompactionStrategy`** — collapses older tool-call groups into concise inline summaries, activated by a message-count trigger
+- **`SummarizationCompactionStrategy`** — uses an LLM to compress older conversation spans into a single summary message, activated by a token-count trigger
+- **`SlidingWindowCompactionStrategy`** — retains only the most recent N user turns and their responses, activated by a turn-count trigger
+- **`TruncationCompactionStrategy`** — emergency backstop that drops the oldest groups until the conversation fits within a hard token budget
+- **`CompactionTriggers`** — factory methods (`MessagesExceed`, `TokensExceed`, `TurnsExceed`, `GroupsExceed`, `HasToolCalls`, `All`, `Any`) that control when each strategy activates
+
+## Concepts
+
+### Message groups
+
+The compaction engine organizes messages into atomic *groups* that are treated as indivisible units during compaction. A group is either:
+
+| Group kind | Contents |
+|---|---|
+| `System` | System prompt message(s) |
+| `User` | A single user message |
+| `ToolCall` | One assistant message with tool calls + the matching tool result messages |
+| `AssistantText` | A single assistant text-only message |
+| `Summary` | One or more messages summarizing earlier conversation spans, produced by compaction strategies |
+
+`Summary` groups (`CompactionGroupKind.Summary`) are created by compaction strategies (for example, `SummarizationCompactionStrategy`) and do not originate directly from user or assistant messages.
+Strategies exclude entire groups rather than individual messages, preserving the tool-call/result pairing required by most model APIs.
+
+### Compaction triggers
+
+A `CompactionTrigger` is a predicate evaluated against the current `MessageIndex`. When the trigger fires, the strategy performs compaction; when it does not fire, the strategy is skipped. Available triggers are:
+
+| Trigger | Activates when… |
+|---|---|
+| `CompactionTriggers.Always` | Always (unconditional) |
+| `CompactionTriggers.Never` | Never (disabled) |
+| `CompactionTriggers.MessagesExceed(n)` | Included message count > n |
+| `CompactionTriggers.TokensExceed(n)` | Included token count > n |
+| `CompactionTriggers.TurnsExceed(n)` | Included user-turn count > n |
+| `CompactionTriggers.GroupsExceed(n)` | Included group count > n |
+| `CompactionTriggers.HasToolCalls()` | At least one included tool-call group exists |
+| `CompactionTriggers.All(...)` | All supplied triggers fire (logical AND) |
+| `CompactionTriggers.Any(...)` | Any supplied trigger fires (logical OR) |
+
+### Pipeline ordering
+
+Order strategies from **least aggressive** to **most aggressive**. The pipeline runs every strategy whose trigger is met. Earlier strategies reduce the conversation gently so that later, more destructive strategies may not need to activate at all.
+
+```
+1. ToolResultCompactionStrategy  – gentle:    replaces verbose tool results with a short label
+2. SummarizationCompactionStrategy – moderate: LLM-summarizes older turns
+3. SlidingWindowCompactionStrategy – aggressive: drops turns beyond the window
+4. TruncationCompactionStrategy   – emergency:  hard token-budget enforcement
+```
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Azure OpenAI service endpoint and model deployment
+- Azure CLI installed and authenticated
+
+**Note**: This sample uses `DefaultAzureCredential`. Sign in with `az login` before running. For production, prefer a specific credential such as `ManagedIdentityCredential`. For more information, see the [Azure CLI authentication documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+## Environment Variables
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"  # Required
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"                      # Optional, defaults to gpt-5.4-mini
+```
+
+## Running the Sample
+
+```powershell
+cd dotnet/samples/02-agents/Agents/Agent_Step18_CompactionPipeline
+dotnet run
+```
+
+## Expected Behavior
+
+The sample runs a seven-turn shopping-assistant conversation with tool calls. After each turn it prints the full message count so you can observe the pipeline compaction doesn't alter the source conversation.
+
+Each of the four compaction strategies has a deliberately low threshold so that it activates during the short demonstration conversation. In a production scenario you would raise the thresholds to match your model's context window and cost requirements.
+
+## Customizing the Pipeline
+
+### Using a single strategy
+
+If you only need one compaction strategy, pass it directly to `CompactionProvider` without wrapping it in a pipeline:
+
+```csharp
+CompactionProvider provider =
+    new(new SlidingWindowCompactionStrategy(CompactionTriggers.TurnsExceed(20)));
+```
+
+### Ad-hoc compaction outside the provider pipeline
+
+`CompactionProvider.CompactAsync` applies a strategy to an arbitrary list of messages without an active agent session:
+
+```csharp
+IEnumerable<ChatMessage> compacted = await CompactionProvider.CompactAsync(
+    new TruncationCompactionStrategy(CompactionTriggers.TokensExceed(8000)),
+    existingMessages);
+```
+
+### Using a different model for summarization
+
+The `SummarizationCompactionStrategy` accepts any `IChatClient`. Use a smaller, cheaper model to reduce summarization cost:
+
+```csharp
+IChatClient summarizerChatClient = openAIClient.GetChatClient("gpt-5.4-mini").AsIChatClient();
+new SummarizationCompactionStrategy(summarizerChatClient, CompactionTriggers.TokensExceed(4000))
+```
+
+### Registering through `ChatClientAgentOptions`
+
+`CompactionProvider` can also be specified directly on `ChatClientAgentOptions` instead of calling `UseAIContextProviders` on the `ChatClientBuilder`:
+
+```csharp
+AIAgent agent = agentChatClient
+    .AsBuilder()
+    .BuildAIAgent(new ChatClientAgentOptions
+    {
+        AIContextProviders = [new CompactionProvider(compactionPipeline)]
+    });
+```
+
+This places the compaction provider at the agent level instead of the chat client level, which allows you to use different compaction strategies for different agents that share the same chat client.
+
+> Note: In this mode the `CompactionProvider` is not engaged during the tool calling loop. Agent-level `AIContextProviders` run before chat history is stored, so any synthetic summary messages produced by `CompactionProvider` can become part of the persisted history when using `ChatHistoryProvider`. If you want to compact only the request context while preserving the original stored history, register `CompactionProvider` on the `ChatClientBuilder` via `UseAIContextProviders(...)` instead of on `ChatClientAgentOptions`.
+
+## Security Considerations
+
+Most compaction strategies in this pipeline (tool-result summarization, sliding window, truncation) only
+remove or reorder existing messages and carry no additional risk. `SummarizationCompactionStrategy` is
+the exception: it calls out to an LLM to produce replacement summary content that permanently becomes
+part of chat history. A compromised or malicious summarization service could return a summary containing
+unsafe instructions, creating a persistent indirect-prompt-injection vector. Using
+`SummarizationCompactionStrategy` is optional and requires explicit configuration — only point its
+`IChatClient` at a summarization service you trust as much as the primary model.

dotnet/samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing/Agent_Step19_InFunctionLoopCheckpointing.csproj

@@ -0,0 +1,16 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing/Program.cs

@@ -0,0 +1,304 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// In-Function Loop Checkpointing — Persist chat history per service call
+//
+// This sample demonstrates how the ChatClientAgent persists chat history after each individual
+// call to the AI service, using the RequirePerServiceCallChatHistoryPersistence option.
+// When an agent uses tools, FunctionInvokingChatClient may loop multiple times
+// (service call → tool execution → service call), and intermediate messages (tool calls and
+// results) are persisted after each service call. This allows you to inspect or recover them
+// even if the process is interrupted mid-loop, but may also result in chat history that is not
+// yet finalized (e.g., tool calls without results) being persisted, which may be undesirable in some cases.
+//
+// Additionally, this sample demonstrates the MessageInjectingChatClient feature, which allows tool
+// code to inject new user messages during the function execution loop. When a tool or anything else enqueues
+// a message via MessageInjectingChatClient.EnqueueMessages during the tool execution loop, the PerServiceCallChatHistoryPersistingChatClient
+// detects the pending message before the next service call and includes the injected message in the request.
+//
+// To use end-of-run persistence instead (atomic run semantics), remove the
+// RequirePerServiceCallChatHistoryPersistence = true setting (or set it to false). End-of-run
+// persistence is the default behavior.
+//
+// The sample runs two multi-turn conversations: one using non-streaming (RunAsync) and one
+// using streaming (RunStreamingAsync), to demonstrate correct behavior in both modes.
+
+using System.ComponentModel;
+using Azure.AI.Extensions.OpenAI;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+var store = Environment.GetEnvironmentVariable("FOUNDRY_RESPONSES_STORE") ?? "false";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+// Define multiple tools so the model makes several tool calls in a single run.
+[Description("Get the current weather for a city.")]
+static string GetWeather([Description("The city name.")] string city) =>
+    city.ToUpperInvariant() switch
+    {
+        "SEATTLE" => "Seattle: 55°F, cloudy with light rain.",
+        "NEW YORK" => "New York: 72°F, sunny and warm.",
+        "LONDON" => "London: 48°F, overcast with fog.",
+        "DUBLIN" => "Dublin: 43°F, overcast with fog.",
+        _ => $"{city}: weather data not available."
+    };
+
+[Description("Get the current time in a city.")]
+static string GetTime([Description("The city name.")] string city) =>
+    city.ToUpperInvariant() switch
+    {
+        "SEATTLE" => "Seattle: 9:00 AM PST",
+        "NEW YORK" => "New York: 12:00 PM EST",
+        "LONDON" => "London: 5:00 PM GMT",
+        "DUBLIN" => "Dublin: 5:00 PM GMT",
+        _ => $"{city}: time data not available."
+    };
+
+// This tool demonstrates message injection during the function execution loop.
+// When called, it checks travel advisories for a city. If an advisory is active, it uses
+// the ambient run context to resolve MessageInjectingChatClient and injects a follow-up user message
+// asking for alternative destinations. The model will process this injected message on the next
+// service call — even though the parent FunctionInvokingChatClient loop would otherwise stop.
+[Description("Check current travel advisories for a city.")]
+static string CheckTravelAdvisory([Description("The city name.")] string city)
+{
+    // Simulated travel advisory data.
+    var advisory = city.ToUpperInvariant() switch
+    {
+        "LONDON" => "Travel advisory: Severe fog warnings in London. Flights may be delayed or cancelled.",
+        "SEATTLE" => "Travel advisory: Heavy rainfall expected. Flooding possible in low-lying areas.",
+        _ => null
+    };
+
+    if (advisory is null)
+    {
+        return $"{city}: No active travel advisories.";
+    }
+
+    // When an advisory is found, inject a follow-up question so the model automatically
+    // suggests alternatives without the user needing to ask.
+    var runContext = AIAgent.CurrentRunContext!;
+    runContext.Agent.GetService<MessageInjectingChatClient>()?.EnqueueMessages(
+        runContext.Session!,
+        [new ChatMessage(ChatRole.User, $"Given the travel advisory for {city}, what alternative cities would you recommend instead?")]);
+
+    return advisory;
+}
+
+// Create the agent — per-service-call persistence is enabled via RequirePerServiceCallChatHistoryPersistence.
+// The in-memory ChatHistoryProvider is used by default when the service does not require service stored chat
+// history, so for those cases, we can inspect the chat history via session.TryGetInMemoryChatHistory().
+var responsesClient = aiProjectClient.GetProjectOpenAIClient().GetProjectResponsesClientForModel(deploymentName);
+IChatClient chatClient = string.Equals(store, "TRUE", StringComparison.OrdinalIgnoreCase) ?
+    responsesClient.AsIChatClient(deploymentName) :
+    responsesClient.AsIChatClientWithStoredOutputDisabled(deploymentName);
+AIAgent agent = chatClient.AsAIAgent(
+    new ChatClientAgentOptions
+    {
+        Name = "WeatherAssistant",
+        RequirePerServiceCallChatHistoryPersistence = true,
+        EnableMessageInjection = true,
+        ChatOptions = new()
+        {
+            Instructions = "You are a helpful travel assistant. When asked about cities, call the appropriate tools for each city.",
+            Tools = [AIFunctionFactory.Create(GetWeather), AIFunctionFactory.Create(GetTime), AIFunctionFactory.Create(CheckTravelAdvisory)]
+        },
+    });
+
+await RunNonStreamingAsync();
+await RunStreamingAsync();
+
+async Task RunNonStreamingAsync()
+{
+    int lastChatHistorySize = 0;
+    string lastConversationId = string.Empty;
+
+    Console.ForegroundColor = ConsoleColor.Yellow;
+    Console.WriteLine("\n=== Non-Streaming Mode ===");
+    Console.ResetColor();
+
+    AgentSession session = await agent.CreateSessionAsync();
+
+    // First turn — ask about multiple cities so the model calls tools.
+    const string Prompt = "What's the weather and time in Seattle, New York, and London?";
+    PrintUserMessage(Prompt);
+
+    var response = await agent.RunAsync(Prompt, session);
+    PrintAgentResponse(response.Text);
+    PrintChatHistory(session, "After run", ref lastChatHistorySize, ref lastConversationId);
+
+    // Second turn — follow-up to verify chat history is correct.
+    const string FollowUp1 = "And Dublin?";
+    PrintUserMessage(FollowUp1);
+
+    response = await agent.RunAsync(FollowUp1, session);
+    PrintAgentResponse(response.Text);
+    PrintChatHistory(session, "After second run", ref lastChatHistorySize, ref lastConversationId);
+
+    // Third turn — follow-up to verify chat history is correct.
+    const string FollowUp2 = "Which city is the warmest?";
+    PrintUserMessage(FollowUp2);
+
+    response = await agent.RunAsync(FollowUp2, session);
+    PrintAgentResponse(response.Text);
+    PrintChatHistory(session, "After third run", ref lastChatHistorySize, ref lastConversationId);
+
+    // Fourth turn — demonstrates message injection during the function loop.
+    // The CheckTravelAdvisory tool detects an advisory for London and injects a follow-up
+    // user message asking for alternative cities. After the tool completes, the internal loop
+    // in PerServiceCallChatHistoryPersistingChatClient detects the pending injected message
+    // and calls the service again, so the model answers the follow-up automatically.
+    const string TravelPrompt = "I'm planning to travel to London next week. Check if there are any travel advisories.";
+    PrintUserMessage(TravelPrompt);
+
+    response = await agent.RunAsync(TravelPrompt, session);
+    PrintAgentResponse(response.Text);
+    PrintChatHistory(session, "After travel advisory run", ref lastChatHistorySize, ref lastConversationId);
+}
+
+async Task RunStreamingAsync()
+{
+    int lastChatHistorySize = 0;
+    string lastConversationId = string.Empty;
+
+    Console.ForegroundColor = ConsoleColor.Yellow;
+    Console.WriteLine("\n=== Streaming Mode ===");
+    Console.ResetColor();
+
+    AgentSession session = await agent.CreateSessionAsync();
+
+    // First turn — ask about multiple cities so the model calls tools.
+    const string Prompt = "What's the weather and time in Seattle, New York, and London?";
+    PrintUserMessage(Prompt);
+
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+
+    await foreach (var update in agent.RunStreamingAsync(Prompt, session))
+    {
+        Console.Write(update);
+
+        // During streaming we should be able to see updates to the chat history
+        // before the full run completes, as each service call is made and persisted.
+        PrintChatHistory(session, "During run", ref lastChatHistorySize, ref lastConversationId);
+    }
+
+    Console.WriteLine();
+    PrintChatHistory(session, "After run", ref lastChatHistorySize, ref lastConversationId);
+
+    // Second turn — follow-up to verify chat history is correct.
+    const string FollowUp1 = "And Dublin?";
+    PrintUserMessage(FollowUp1);
+
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+
+    await foreach (var update in agent.RunStreamingAsync(FollowUp1, session))
+    {
+        Console.Write(update);
+
+        // During streaming we should be able to see updates to the chat history
+        // before the full run completes, as each service call is made and persisted.
+        PrintChatHistory(session, "During second run", ref lastChatHistorySize, ref lastConversationId);
+    }
+
+    Console.WriteLine();
+    PrintChatHistory(session, "After second run", ref lastChatHistorySize, ref lastConversationId);
+
+    // Third turn — follow-up to verify chat history is correct.
+    const string FollowUp2 = "Which city is the warmest?";
+    PrintUserMessage(FollowUp2);
+
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+
+    await foreach (var update in agent.RunStreamingAsync(FollowUp2, session))
+    {
+        Console.Write(update);
+
+        // During streaming we should be able to see updates to the chat history
+        // before the full run completes, as each service call is made and persisted.
+        PrintChatHistory(session, "During third run", ref lastChatHistorySize, ref lastConversationId);
+    }
+
+    Console.WriteLine();
+    PrintChatHistory(session, "After third run", ref lastChatHistorySize, ref lastConversationId);
+
+    // Fourth turn — demonstrates message injection during the function loop (streaming).
+    // The CheckTravelAdvisory tool detects an advisory for London and injects a follow-up
+    // user message asking for alternative cities. After the tool completes, the internal loop
+    // in PerServiceCallChatHistoryPersistingChatClient detects the pending injected message
+    // and calls the service again, so the model answers the follow-up automatically.
+    const string TravelPrompt = "I'm planning to travel to London next week. Check if there are any travel advisories.";
+    PrintUserMessage(TravelPrompt);
+
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+
+    await foreach (var update in agent.RunStreamingAsync(TravelPrompt, session))
+    {
+        Console.Write(update);
+
+        // During streaming we should be able to see updates to the chat history
+        // before the full run completes, as each service call is made and persisted.
+        PrintChatHistory(session, "During travel advisory run", ref lastChatHistorySize, ref lastConversationId);
+    }
+
+    Console.WriteLine();
+    PrintChatHistory(session, "After travel advisory run", ref lastChatHistorySize, ref lastConversationId);
+}
+
+void PrintUserMessage(string message)
+{
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[User] ");
+    Console.ResetColor();
+    Console.WriteLine(message);
+}
+
+void PrintAgentResponse(string? text)
+{
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+    Console.WriteLine(text);
+}
+
+// Helper to print the current chat history from the session.
+void PrintChatHistory(AgentSession session, string label, ref int lastChatHistorySize, ref string lastConversationId)
+{
+    if (session.TryGetInMemoryChatHistory(out var history) && history.Count != lastChatHistorySize)
+    {
+        Console.ForegroundColor = ConsoleColor.DarkGray;
+        Console.WriteLine($"\n  [{label} — Chat history: {history.Count} message(s)]");
+        foreach (var msg in history)
+        {
+            var preview = msg.Text?.Length > 80 ? msg.Text[..80] + "…" : msg.Text;
+            var contentTypes = string.Join(", ", msg.Contents.Select(c => c.GetType().Name));
+            Console.WriteLine($"    {msg.Role,-12} | {(string.IsNullOrWhiteSpace(preview) ? $"[{contentTypes}]" : preview)}");
+        }
+
+        Console.ResetColor();
+
+        lastChatHistorySize = history.Count;
+    }
+
+    if (session is ChatClientAgentSession ccaSession && ccaSession.ConversationId is not null && ccaSession.ConversationId != lastConversationId)
+    {
+        Console.ForegroundColor = ConsoleColor.DarkGray;
+        Console.WriteLine($"  [{label} — Conversation ID: {ccaSession.ConversationId}]");
+        Console.ResetColor();
+        lastConversationId = ccaSession.ConversationId;
+    }
+}

dotnet/samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing/README.md

@@ -0,0 +1,66 @@
+# In-Function-Loop Checkpointing
+
+This sample demonstrates how `ChatClientAgent` can persist chat history after each individual call to the AI service using the `RequirePerServiceCallChatHistoryPersistence` option. This per-service-call persistence ensures intermediate progress is saved during the function invocation loop.
+
+## What This Sample Shows
+
+When an agent uses tools, the `FunctionInvokingChatClient` loops multiple times (service call → tool execution → service call → …). By enabling `RequirePerServiceCallChatHistoryPersistence = true`, chat history is persisted after each service call via the `PerServiceCallChatHistoryPersistingChatClient` decorator:
+
+- A `PerServiceCallChatHistoryPersistingChatClient` decorator is inserted into the chat client pipeline
+- Before each service call, the decorator loads history from the `ChatHistoryProvider` and prepends it to the request
+- After each service call, the decorator notifies the `ChatHistoryProvider` (and any `AIContextProvider` instances) with the new messages
+- Only **new** messages are sent to providers on each notification — messages that were already persisted in an earlier call within the same run are deduplicated automatically
+
+By default (without `RequirePerServiceCallChatHistoryPersistence`), chat history is persisted at the end of the full agent run instead. To use per-service-call persistence, set `RequirePerServiceCallChatHistoryPersistence = true` on `ChatClientAgentOptions`.
+
+With `RequirePerServiceCallChatHistoryPersistence` = true, the behavior matches that of chat history stored in the underlying AI service exactly.
+
+Per-service-call persistence is useful for:
+- **Crash recovery** — if the process is interrupted mid-loop, the intermediate tool calls and results are already persisted
+- **Observability** — you can inspect the chat history while the agent is still running (e.g., during streaming)
+- **Long-running tool loops** — agents with many sequential tool calls benefit from incremental persistence
+
+## How It Works
+
+The sample asks the agent about the weather and time in three cities. The model calls the `GetWeather` and `GetTime` tools for each city, resulting in multiple service calls within a single `RunStreamingAsync` invocation. After the run completes, the sample prints the full chat history to show all the intermediate messages that were persisted along the way.
+
+### Pipeline Architecture
+
+```
+ChatClientAgent
+  └─ FunctionInvokingChatClient    (handles tool call loop)
+       └─ PerServiceCallChatHistoryPersistingChatClient  (persists after each service call)
+            └─ Leaf IChatClient            (Azure OpenAI)
+```
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Azure OpenAI service endpoint and model deployment
+- Azure CLI installed and authenticated
+
+**Note**: This sample uses `DefaultAzureCredential`. Sign in with `az login` before running. For production, prefer a specific credential such as `ManagedIdentityCredential`. For more information, see the [Azure CLI authentication documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+## Environment Variables
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"  # Required
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"                      # Optional, defaults to gpt-5.4-mini
+```
+
+## Running the Sample
+
+```powershell
+cd dotnet/samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing
+dotnet run
+```
+
+## Expected Behavior
+
+The sample runs two conversation turns:
+
+1. **First turn** — asks about weather and time in three cities. The model calls `GetWeather` and `GetTime` tools (potentially in parallel or sequentially), then provides a summary. The chat history dump after the run shows all the intermediate tool call and result messages.
+
+2. **Second turn** — asks a follow-up question ("Which city is the warmest?") that uses the persisted conversation context. The chat history dump shows the full accumulated conversation.
+
+The chat history printout uses `session.TryGetInMemoryChatHistory()` to inspect the in-memory storage.

dotnet/samples/02-agents/Agents/Agent_Step20_DynamicFunctionTools/Agent_Step20_DynamicFunctionTools.csproj

@@ -0,0 +1,16 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step20_DynamicFunctionTools/Program.cs

@@ -0,0 +1,282 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to dynamically expand the set of function tools available to an
+// agent during a function-calling loop. The agent starts with a single "RequestTools" function.
+// When the model calls RequestTools with a description of the capabilities needed, the function
+// uses the ambient FunctionInvocationContext to add new tools to ChatOptions.Tools. The agent
+// can then use the newly added tools in subsequent iterations of the same function-calling loop.
+
+using System.ComponentModel;
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// Pre-defined tool implementations that can be loaded on demand.
+[Description("Get the current weather for a city.")]
+static string GetWeather([Description("The city name.")] string city) =>
+    city.ToUpperInvariant() switch
+    {
+        "SEATTLE" => "Seattle: 55°F, cloudy with light rain.",
+        "NEW YORK" => "New York: 72°F, sunny and warm.",
+        "LONDON" => "London: 48°F, overcast with fog.",
+        _ => $"{city}: weather data not available, please provide one of the following city names: 'Seattle', 'New York', 'London'."
+    };
+
+[Description("Get the current local time for a city.")]
+static string GetTime([Description("The city name.")] string city) =>
+    city.ToUpperInvariant() switch
+    {
+        "SEATTLE" => "Seattle: 9:00 AM PST",
+        "NEW YORK" => "New York: 12:00 PM EST",
+        "LONDON" => "London: 5:00 PM GMT",
+        _ => $"{city}: time data not available, please provide one of the following city names: 'Seattle', 'New York', 'London'."
+    };
+
+[Description("Convert a temperature from Fahrenheit to Celsius.")]
+static string ConvertFahrenheitToCelsius([Description("The temperature in Fahrenheit.")] double fahrenheit) =>
+    $"{fahrenheit}°F = {(fahrenheit - 32) * 5 / 9:F1}°C";
+
+// A registry of tool sets that can be loaded by description keyword.
+Dictionary<string, List<AITool>> toolCatalog = new(StringComparer.OrdinalIgnoreCase)
+{
+    ["weather"] = [AIFunctionFactory.Create(GetWeather, name: "GetWeather")],
+    ["time"] = [AIFunctionFactory.Create(GetTime, name: "GetTime")],
+    ["temperature"] = [AIFunctionFactory.Create(ConvertFahrenheitToCelsius, name: "ConvertFahrenheitToCelsius")],
+};
+
+// The RequestTools function uses the ambient FunctionInvocationContext to add tools dynamically.
+AIFunction requestToolsFunction = AIFunctionFactory.Create(
+    [Description("Request additional tools to be loaded based on a description of the functionality needed. " +
+                 "Call this when you need capabilities that are not yet available in your current tool set.")] (
+        [Description("A description of the functionality required, e.g. 'weather', 'time', or 'temperature conversion'.")] string description
+    ) =>
+    {
+        // Access the ambient FunctionInvocationContext provided by FunctionInvokingChatClient.
+        var context = FunctionInvokingChatClient.CurrentContext
+            ?? throw new InvalidOperationException("No ambient FunctionInvocationContext available.");
+
+        var tools = context.Options?.Tools;
+        if (tools is null)
+        {
+            return "Unable to register new tools: ChatOptions.Tools is not available.";
+        }
+
+        // Find matching tool sets from the catalog.
+        List<string> addedToolNames = [];
+        foreach (var kvp in toolCatalog)
+        {
+            var keyword = kvp.Key;
+            var catalogTools = kvp.Value;
+            if (description.Contains(keyword, StringComparison.OrdinalIgnoreCase))
+            {
+                foreach (var tool in catalogTools)
+                {
+                    // Avoid adding duplicates.
+                    if (tool is AIFunction fn && !tools.Any(t => t is AIFunction existing && existing.Name == fn.Name))
+                    {
+                        tools.Add(tool);
+                        addedToolNames.Add(fn.Name);
+                    }
+                }
+            }
+        }
+
+        return addedToolNames.Count > 0
+            ? "Successfully loaded tools"
+            : $"No tools matched the description '{description}'. Available categories: {string.Join(", ", toolCatalog.Keys)}.";
+    },
+    name: "RequestTools");
+
+// Create the agent with only the RequestTools function initially.
+// Insert chat client middleware that logs the tools available on each LLM call,
+// making the dynamic expansion visible in the console output.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AIProjectClient(
+    new Uri(endpoint),
+    new DefaultAzureCredential())
+    .AsAIAgent(
+        model: deploymentName,
+        instructions: """
+            You are a helpful assistant. You start with limited tools.
+            When you need functionality that you don't currently have, call RequestTools with a description
+            of what you need. After new tools are loaded, use them to answer the user's question.
+            """,
+        tools: [requestToolsFunction],
+        clientFactory: (chatClient) => chatClient
+            .AsBuilder()
+            .Use(getResponseFunc: ToolLoggingMiddleware, getStreamingResponseFunc: ToolLoggingStreamingMiddleware)
+            .Build());
+
+// Run a conversation that triggers dynamic tool expansion.
+Console.WriteLine("=== Dynamic Function Tools Sample ===\n");
+
+string[] prompts =
+[
+    "What's the weather like in Seattle and London?",
+    "What time is it in New York?",
+    "Can you convert those temperatures to Celsius?"
+];
+
+// --- Non-Streaming Mode ---
+Console.ForegroundColor = ConsoleColor.Yellow;
+Console.WriteLine("=== Non-Streaming Mode ===");
+Console.ResetColor();
+Console.WriteLine();
+
+AgentSession session = await agent.CreateSessionAsync();
+
+foreach (var prompt in prompts)
+{
+    Console.ForegroundColor = ConsoleColor.Green;
+    Console.Write("[User] ");
+    Console.ResetColor();
+    Console.WriteLine(prompt);
+
+    var response = await agent.RunAsync(prompt, session);
+
+    // Print all message contents including tool calls, tool results, and text.
+    foreach (var message in response.Messages)
+    {
+        foreach (var content in message.Contents)
+        {
+            switch (content)
+            {
+                case FunctionCallContent functionCall:
+                    Console.ForegroundColor = ConsoleColor.Yellow;
+                    Console.WriteLine($"  [Tool Call] {functionCall.Name}({string.Join(", ", functionCall.Arguments?.Select(a => $"{a.Key}: {a.Value}") ?? [])})");
+                    Console.ResetColor();
+                    break;
+
+                case FunctionResultContent functionResult:
+                    Console.ForegroundColor = ConsoleColor.DarkYellow;
+                    Console.WriteLine($"  [Tool Result] {functionResult.CallId} => {functionResult.Result}");
+                    Console.ResetColor();
+                    break;
+
+                case TextContent textContent when !string.IsNullOrWhiteSpace(textContent.Text):
+                    Console.ForegroundColor = ConsoleColor.Cyan;
+                    Console.Write("[Agent] ");
+                    Console.ResetColor();
+                    Console.WriteLine(textContent.Text);
+                    break;
+            }
+        }
+    }
+
+    Console.WriteLine();
+}
+
+// --- Streaming Mode ---
+Console.ForegroundColor = ConsoleColor.Yellow;
+Console.WriteLine("=== Streaming Mode ===");
+Console.ResetColor();
+Console.WriteLine();
+
+AgentSession streamingSession = await agent.CreateSessionAsync();
+
+foreach (var prompt in prompts)
+{
+    Console.ForegroundColor = ConsoleColor.Green;
+    Console.Write("[User] ");
+    Console.ResetColor();
+    Console.WriteLine(prompt);
+
+    bool inAgentText = false;
+
+    await foreach (var update in agent.RunStreamingAsync(prompt, streamingSession))
+    {
+        foreach (var content in update.Contents)
+        {
+            switch (content)
+            {
+                case FunctionCallContent functionCall:
+                    if (inAgentText)
+                    {
+                        Console.WriteLine();
+                        inAgentText = false;
+                    }
+
+                    Console.ForegroundColor = ConsoleColor.Yellow;
+                    Console.WriteLine($"  [Tool Call] {functionCall.Name}({string.Join(", ", functionCall.Arguments?.Select(a => $"{a.Key}: {a.Value}") ?? [])})");
+                    Console.ResetColor();
+                    break;
+
+                case FunctionResultContent functionResult:
+                    Console.ForegroundColor = ConsoleColor.DarkYellow;
+                    Console.WriteLine($"  [Tool Result] {functionResult.CallId} => {functionResult.Result}");
+                    Console.ResetColor();
+                    break;
+
+                case TextContent textContent when !string.IsNullOrWhiteSpace(textContent.Text):
+                    if (!inAgentText)
+                    {
+                        Console.ForegroundColor = ConsoleColor.Cyan;
+                        Console.Write("[Agent] ");
+                        Console.ResetColor();
+                        inAgentText = true;
+                    }
+
+                    Console.Write(textContent.Text);
+                    break;
+            }
+        }
+    }
+
+    if (inAgentText)
+    {
+        Console.WriteLine();
+    }
+
+    Console.WriteLine();
+}
+
+// Chat client middleware that logs the number and names of tools on each LLM request.
+async Task<ChatResponse> ToolLoggingMiddleware(
+    IEnumerable<ChatMessage> messages,
+    ChatOptions? options,
+    IChatClient innerChatClient,
+    CancellationToken cancellationToken)
+{
+    LogTools(options);
+
+    return await innerChatClient.GetResponseAsync(messages, options, cancellationToken);
+}
+
+// Streaming version of the tool logging middleware.
+async IAsyncEnumerable<ChatResponseUpdate> ToolLoggingStreamingMiddleware(
+    IEnumerable<ChatMessage> messages,
+    ChatOptions? options,
+    IChatClient innerChatClient,
+    [System.Runtime.CompilerServices.EnumeratorCancellation] CancellationToken cancellationToken)
+{
+    LogTools(options);
+
+    await foreach (var update in innerChatClient.GetStreamingResponseAsync(messages, options, cancellationToken))
+    {
+        yield return update;
+    }
+}
+
+// Shared helper to log the current tool set.
+void LogTools(ChatOptions? options)
+{
+    if (options?.Tools is { Count: > 0 } tools)
+    {
+        var toolNames = tools.OfType<AIFunction>().Select(t => t.Name);
+        Console.ForegroundColor = ConsoleColor.DarkGray;
+        Console.WriteLine($"  [Middleware] LLM call with {tools.Count} tool(s): {string.Join(", ", toolNames)}");
+        Console.ResetColor();
+    }
+    else
+    {
+        Console.ForegroundColor = ConsoleColor.DarkGray;
+        Console.WriteLine("  [Middleware] LLM call with 0 tools");
+        Console.ResetColor();
+    }
+}

dotnet/samples/02-agents/Agents/Agent_Step20_DynamicFunctionTools/README.md

@@ -0,0 +1,38 @@
+# Dynamic Function Tools
+
+This sample demonstrates how to dynamically expand the set of function tools available to an agent during a function-calling loop.
+
+## What it demonstrates
+
+- The agent starts with only a single `RequestTools` function
+- When the model needs capabilities it doesn't have, it calls `RequestTools` with a description of the functionality needed
+- The `RequestTools` function uses the ambient `FunctionInvokingChatClient.CurrentContext` to access `ChatOptions.Tools` and add new tools at runtime
+- The agent then uses the newly added tools in subsequent iterations of the same function-calling loop
+
+## How it works
+
+1. A tool catalog maps keywords (e.g. "weather", "time", "temperature") to pre-built `AIFunction` instances
+2. The `RequestTools` function matches the description against catalog keywords and adds matching tools to `ChatOptions.Tools`
+3. `FunctionInvokingChatClient` automatically picks up the new tools on the next iteration of its loop
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Azure OpenAI service endpoint and deployment configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+- User has the `Cognitive Services OpenAI Contributor` role for the Azure OpenAI resource
+
+## Running the sample
+
+Set the required environment variables:
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
+```
+
+Run the sample:
+
+```powershell
+dotnet run
+```

dotnet/samples/02-agents/Agents/Agent_Step21_ShellWithEnvironment/Agent_Step21_ShellWithEnvironment.csproj

@@ -0,0 +1,17 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.Identity" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Tools.Shell\Microsoft.Agents.AI.Tools.Shell.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step21_ShellWithEnvironment/Program.cs

@@ -0,0 +1,133 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// Shell tool with environment-aware system prompt
+//
+// WARNING: This sample uses LocalShellExecutor, which executes real commands
+// against the shell on this machine. Approval gating is disabled here so
+// the demo runs unattended; in any real application keep approval on
+// (the default), or use DockerShellExecutor for container isolation. The
+// commands the model emits below are read-only or scoped (echo, cd into
+// a temp folder, set a process-local env var) but a different model or
+// prompt could choose to do something destructive. Run this only in an
+// environment where you are comfortable with the agent typing into your
+// terminal.
+//
+// Demonstrates LocalShellExecutor in both modes paired with
+// ShellEnvironmentProvider, an AIContextProvider that probes the live
+// shell (OS, family, version, CWD, common CLIs) and injects authoritative
+// system-prompt instructions so the agent emits commands in the right
+// idiom (PowerShell vs POSIX).
+//
+// Two runs:
+//   1) Stateless mode: each tool call runs in a fresh shell. Useful when
+//      commands are independent (read-only scripts, version checks, file
+//      listings) and you want strong isolation between calls. Side
+//      effects in one call (cd, exported variables) do NOT carry to the
+//      next.
+//   2) Persistent mode: a single long-lived shell is reused across calls,
+//      so working directory and exported environment variables are
+//      preserved. Useful for multi-step workflows that build state
+//      (cd into a folder and run a sequence of commands there; set a
+//      token in one step and read it in the next).
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Tools.Shell;
+using Microsoft.Extensions.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+var aiProjectClient = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential());
+
+const string Instructions = """
+    You are an agent with a single tool: run_shell. Use it to satisfy the
+    user's request. Do not describe what you would do — actually run the
+    commands. Reply with the final answer derived from real output.
+    """;
+
+// --------------------------------------------------------------------
+// 1. Stateless mode — each call gets a fresh shell.
+// --------------------------------------------------------------------
+Console.WriteLine("### Stateless mode\n");
+await using (var statelessShell = new LocalShellExecutor(new() { Mode = ShellMode.Stateless, AcknowledgeUnsafe = true }))
+{
+    var envProvider = new ShellEnvironmentProvider(statelessShell);
+    var statelessAgent = aiProjectClient.AsAIAgent(new ChatClientAgentOptions
+    {
+        ChatOptions = new()
+        {
+            ModelId = deploymentName,
+            Instructions = Instructions,
+            Tools = [statelessShell.AsAIFunction(requireApproval: false)],
+        },
+        AIContextProviders = [envProvider],
+    });
+
+    var statelessSession = await statelessAgent.CreateSessionAsync();
+    Console.WriteLine(await statelessAgent.RunAsync("Print the current working directory.", statelessSession));
+    Console.WriteLine();
+
+    // Show that side effects do NOT carry between stateless calls: ask the
+    // agent to cd into the system temp directory in one call, then ask
+    // for the CWD in a second call. Stateless mode means the cd is gone.
+    Console.WriteLine(await statelessAgent.RunAsync("Change directory into the system temp folder, then print the current working directory.", statelessSession));
+    Console.WriteLine();
+    Console.WriteLine(await statelessAgent.RunAsync("In a NEW shell call, print the current working directory again. Tell me whether it matches the temp folder from the previous call.", statelessSession));
+    Console.WriteLine();
+
+    PrintSnapshot(envProvider.CurrentSnapshot!);
+}
+
+// --------------------------------------------------------------------
+// 2. Persistent mode — one shell, reused across calls. State carries.
+// --------------------------------------------------------------------
+Console.WriteLine("\n### Persistent mode\n");
+await using (var persistentShell = new LocalShellExecutor(new() { Mode = ShellMode.Persistent, AcknowledgeUnsafe = true }))
+{
+    var envProvider = new ShellEnvironmentProvider(persistentShell);
+    var persistentAgent = aiProjectClient.AsAIAgent(new ChatClientAgentOptions
+    {
+        ChatOptions = new()
+        {
+            ModelId = deploymentName,
+            Instructions = Instructions,
+            Tools = [persistentShell.AsAIFunction(requireApproval: false)],
+        },
+        AIContextProviders = [envProvider],
+    });
+
+    var persistentSession = await persistentAgent.CreateSessionAsync();
+
+    // State carries across calls in persistent mode: cd into temp, then
+    // verify the next call sees the new CWD.
+    Console.WriteLine(await persistentAgent.RunAsync("Change directory into the system temp folder, then print the current working directory.", persistentSession));
+    Console.WriteLine();
+    Console.WriteLine(await persistentAgent.RunAsync("In a NEW shell call, print the current working directory again. Tell me whether it still matches the temp folder.", persistentSession));
+    Console.WriteLine();
+
+    // Same idea with an exported variable: set in one call, read in the next.
+    Console.WriteLine(await persistentAgent.RunAsync("Set the environment variable DEMO_TOKEN to the value 'hello-world'.", persistentSession));
+    Console.WriteLine();
+    Console.WriteLine(await persistentAgent.RunAsync("Print the current value of DEMO_TOKEN. Tell me exactly what value the shell reports.", persistentSession));
+    Console.WriteLine();
+
+    PrintSnapshot(envProvider.CurrentSnapshot!);
+}
+
+static void PrintSnapshot(ShellEnvironmentSnapshot snap)
+{
+    Console.WriteLine("--- Captured environment snapshot ---");
+    Console.WriteLine($"  Family:  {snap.Family}");
+    Console.WriteLine($"  OS:      {snap.OSDescription}");
+    Console.WriteLine($"  Shell:   {snap.ShellVersion ?? "(unknown)"}");
+    Console.WriteLine($"  CWD:     {snap.WorkingDirectory}");
+    foreach (var (tool, version) in snap.ToolVersions)
+    {
+        Console.WriteLine($"  {tool,-8} {version ?? "(not installed)"}");
+    }
+}

dotnet/samples/02-agents/Agents/README.md

@@ -0,0 +1,90 @@
+# Getting started with agents
+
+The getting started with agents samples demonstrate the fundamental concepts and functionalities
+of single agents and can be used with any agent type.
+
+While the functionality can be used with any agent type, these samples are configured for
+Microsoft Foundry using `AIProjectClient`.
+
+For other samples that demonstrate how to create and configure each type of agent that come with the agent framework,
+see the [How to create an agent for each provider](../AgentProviders/README.md) samples.
+
+## Getting started with agents prerequisites
+
+Before you begin, ensure you have the following prerequisites:
+
+- .NET 10 SDK or later
+- Microsoft Foundry project endpoint and model configured
+- Azure CLI installed and authenticated (for Azure credential authentication)
+- User has the required role to invoke models in the Foundry project.
+
+**Note**: These samples use models hosted through Microsoft Foundry. For more information, see [Azure AI Foundry documentation](https://learn.microsoft.com/en-us/azure/ai-foundry/).
+
+**Note**: These samples use Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Foundry project. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+## Samples
+
+|Sample|Description|
+|---|---|
+|[Using OpenAPI function tools with a simple agent](https://github.com/microsoft/semantic-kernel/tree/main/dotnet/samples/AgentFrameworkMigration/AzureOpenAI/Step04_ToolCall_WithOpenAPI)|This sample demonstrates how to create function tools from an OpenAPI spec and use them with a simple agent (note that this sample is in the Semantic Kernel repository)|
+|[Using function tools with approvals](./Agent_Step01_UsingFunctionToolsWithApprovals/)|This sample demonstrates how to use function tools where approvals require human in the loop approvals before execution|
+|[Structured output with a simple agent](./Agent_Step02_StructuredOutput/)|This sample demonstrates how to use structured output with a simple agent|
+|[Persisted conversations with a simple agent](./Agent_Step03_PersistedConversations/)|This sample demonstrates how to persist conversations and reload them later. This is useful for cases where an agent is hosted in a stateless service|
+|[3rd party chat history storage with a simple agent](./Agent_Step04_3rdPartyChatHistoryStorage/)|This sample demonstrates how to store chat history in a 3rd party storage solution|
+|[Observability with a simple agent](./Agent_Step05_Observability/)|This sample demonstrates how to add telemetry to a simple agent|
+|[Dependency injection with a simple agent](./Agent_Step06_DependencyInjection/)|This sample demonstrates how to add and resolve an agent with a dependency injection container|
+|[Exposing a simple agent as MCP tool](./Agent_Step07_AsMcpTool/)|This sample demonstrates how to expose an agent as an MCP tool|
+|[Using images with a simple agent](./Agent_Step08_UsingImages/)|This sample demonstrates how to use image multi-modality with an AI agent|
+|[Exposing a simple agent as a function tool](./Agent_Step09_AsFunctionTool/)|This sample demonstrates how to expose an agent as a function tool|
+|[Background responses with tools and persistence](./Agent_Step10_BackgroundResponsesWithToolsAndPersistence/)|This sample demonstrates advanced background response scenarios including function calling during background operations and state persistence|
+|[Using middleware with an agent](./Agent_Step11_Middleware/)|This sample demonstrates how to use middleware with an agent|
+|[Using plugins with an agent](./Agent_Step12_Plugins/)|This sample demonstrates how to use plugins with an agent|
+|[Reducing chat history size](./Agent_Step13_ChatReduction/)|This sample demonstrates how to reduce the chat history to constrain its size, where chat history is maintained locally|
+|[Background responses](./Agent_Step14_BackgroundResponses/)|This sample demonstrates how to use background responses for long-running operations with polling and resumption support|
+|[Deep research with an agent](./Agent_Step15_DeepResearch/)|This sample demonstrates how to use the Deep Research Tool to perform comprehensive research on complex topics|
+|[Declarative agent](./Agent_Step16_Declarative/)|This sample demonstrates how to declaratively define an agent.|
+|[Providing additional AI Context to an agent using multiple AIContextProviders](./Agent_Step17_AdditionalAIContext/)|This sample demonstrates how to inject additional AI context into a ChatClientAgent using multiple custom AIContextProvider components that are attached to the agent.|
+|[Using compaction pipeline with an agent](./Agent_Step18_CompactionPipeline/)|This sample demonstrates how to use a compaction pipeline to efficiently limit the size of the conversation history for an agent.|
+|[In-function-loop checkpointing](./Agent_Step19_InFunctionLoopCheckpointing/)|This sample demonstrates how to persist chat history after each service call during a tool-calling loop, enabling crash recovery and mid-run observability.|
+|[Dynamic function tools](./Agent_Step20_DynamicFunctionTools/)|This sample demonstrates how to dynamically expand the set of function tools available to an agent during a function-calling loop using the ambient FunctionInvocationContext.|
+
+## Running the samples from the console
+
+To run the samples, navigate to the desired sample directory, e.g.
+
+```powershell
+cd Agent_Step01_UsingFunctionToolsWithApprovals
+```
+
+Set the following environment variables:
+
+```powershell
+$env:FOUNDRY_PROJECT_ENDPOINT="https://<your-project>.services.ai.azure.com/api/projects/<your-project>" # Replace with your Foundry project endpoint
+$env:FOUNDRY_MODEL="gpt-5.4-mini"  # Optional, defaults to gpt-5.4-mini
+```
+
+If the variables are not set, you will be prompted for the values when running the samples.
+
+Execute the following command to build the sample:
+
+```powershell
+dotnet build
+```
+
+Execute the following command to run the sample:
+
+```powershell
+dotnet run --no-build
+```
+
+Or just build and run in one step:
+
+```powershell
+dotnet run
+```
+
+## Running the samples from Visual Studio
+
+Open the solution in Visual Studio and set the desired sample project as the startup project. Then, run the project using the built-in debugger or by pressing `F5`.
+
+You will be prompted for any required environment variables if they are not already set.