dotnet-microsoft-agent-framework

name	dotnet-microsoft-agent-framework
description	Integrates AI/LLM via Microsoft Agent Framework. Agents, workflows, tools, MCP servers, multi-agent orchestration.
metadata	{"short-description":".NET skill guidance for AI agent development"}

dotnet-microsoft-agent-framework

Microsoft Agent Framework for building agentic AI applications in .NET. Covers agents, workflows, multi-agent orchestration, tool integration (MCP servers, function calling), provider configuration (Azure OpenAI, OpenAI, Microsoft Foundry, Anthropic, Ollama), chat history management, middleware, and enterprise features (observability, authentication, responsible AI).

Scope

• Agent creation and configuration for all supported providers
• Workflow orchestration (sequential, concurrent, group chat, handoff, magentic)
• Tool integration (MCP servers, function calling, hosted tools)
• Chat history and session management
• Middleware for intercepting agent actions
• Enterprise observability with OpenTelemetry
• Authentication with Microsoft Entra
• Responsible AI features (prompt injection protection, content safety)

Out of scope

• General async/await patterns and cancellation token propagation -- see [skill:dotnet-csharp-async-patterns]
• DI container mechanics and service lifetime management -- see [skill:dotnet-csharp-dependency-injection]
• HTTP client resilience and retry policies -- see [skill:dotnet-resilience]
• Configuration binding (options pattern, secrets) -- see [skill:dotnet-csharp-configuration]

Cross-references: [skill:dotnet-csharp-async-patterns] for async streaming patterns used with chat completions, [skill:dotnet-csharp-dependency-injection] for agent service registration in ASP.NET Core, [skill:dotnet-resilience] for retry policies on AI service calls, [skill:dotnet-csharp-configuration] for managing API keys and model configuration.

---

Package Landscape

Package	Purpose
Microsoft.Agents.AI	Core abstractions, AIAgent base class
Microsoft.Agents.AI.OpenAI	OpenAI provider support (Chat Completion, Responses, Assistants)
Microsoft.Agents.AI.AzureAI	Azure OpenAI provider support
Microsoft.Agents.AI.AzureAI.Persistent	Microsoft Foundry Agent Service integration
Microsoft.Agents.AI.Anthropic	Anthropic provider support
Microsoft.Agents.AI.Ollama	Ollama local model integration
Microsoft.Agents.Hosting	Hosting abstractions for agent applications
Microsoft.Agents.AI.Hosting.OpenAI	OpenAI-compatible endpoint hosting
Microsoft.Agents.Middleware	Middleware pipeline for agent interception
Azure.Identity	Azure authentication (DefaultAzureCredential, ManagedIdentity)

---

Agents

OpenAI

The OpenAI provider supports three client types with different capabilities:

Client Type	API	Best For
Chat Completion	Chat Completions API	Simple agents, broad model support
Responses	Responses API	Full-featured agents with hosted tools
Assistants	Assistants API	Server-managed agents with persistent threads

Chat Completion Agent

using Microsoft.Agents.AI;
using OpenAI;

OpenAIClient client = new OpenAIClient("<your_api_key>");
var chatClient = client.GetChatClient("gpt-4o-mini");

AIAgent agent = chatClient.AsAIAgent(
    instructions: "You are a helpful assistant specialized in data analysis.",
    name: "DataAnalyst");

var response = await agent.RunAsync("Analyze Q3 sales trends.");
Console.WriteLine(response);

Responses API Agent

using Microsoft.Agents.AI.OpenAI;
using OpenAI;

OpenAIClient client = new OpenAIClient("<your_api_key>");
var responsesClient = client.GetResponsesClient("gpt-4o");

AIAgent agent = responsesClient.AsAIAgent(
    instructions: "You are a research assistant with access to web search.",
    name: "ResearchAssistant");

// Responses API supports hosted tools (web search, file search, code interpreter)
var response = await agent.RunAsync("Find recent articles about .NET 10 features.");

OpenAI Assistants

using Microsoft.Agents.AI.OpenAI;
using OpenAI;

OpenAIClient client = new OpenAIClient("<your_api_key>");
var assistantClient = client.GetAssistantClient();

// Create a persistent assistant with built-in tools
var assistant = await assistantClient.CreateAssistantAsync(
    model: "gpt-4o",
    name: "CodeReviewer",
    instructions: "You review code for best practices and potential issues.",
    tools: ["code_interpreter"]);

AIAgent agent = assistant.AsAIAgent();

Azure OpenAI

Azure OpenAI provides enterprise-grade AI with managed endpoints, private networking, and Microsoft Entra authentication.

using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;

AzureOpenAIClient client = new AzureOpenAIClient(
    new Uri("https://<resource>.openai.azure.com"),
    new DefaultAzureCredential());

var chatClient = client.GetChatClient("gpt-4o");

AIAgent agent = chatClient.AsAIAgent(
    instructions: "You are a customer support agent for Contoso.");

var response = await agent.RunAsync("I need help with my order.");

Azure OpenAI also supports Responses and Assistants APIs:

// Responses API
var responsesClient = client.GetResponsesClient("gpt-4o");
AIAgent agent = responsesClient.AsAIAgent(instructions: "...");

// Assistants API
var assistantClient = client.GetAssistantClient();
var assistant = await assistantClient.CreateAssistantAsync(model: "gpt-4o", ...);
AIAgent agent = assistant.AsAIAgent();

Microsoft Foundry Agent Service

Foundry Agent Service provides managed, scalable agents with built-in content safety, persistent storage, and Microsoft 365 integration.

using Azure.AI.Agents.Persistent;
using Azure.Identity;
using Microsoft.Agents.AI;

var persistentAgentsClient = new PersistentAgentsClient(
    "https://<resource>.services.ai.azure.com/api/projects/<project>",
    new DefaultAzureCredential());

// Create a new agent in the service
AIAgent agent = await persistentAgentsClient.CreateAIAgentAsync(
    model: "gpt-4o-mini",
    name: "SupportBot",
    instructions: "You handle customer support inquiries.");

// Use the agent
var response = await agent.RunAsync("My account is locked.");

// Or retrieve an existing agent
AIAgent existingAgent = await persistentAgentsClient.GetAIAgentAsync("<agent-id>");

Anthropic

using Microsoft.Agents.AI.Anthropic;
using Anthropic;

var client = new AnthropicClient("<api_key>");
var agent = client.AsAIAgent(
    model: "claude-3-opus-20240229",
    instructions: "You are a helpful assistant.");

var response = await agent.RunAsync("Explain quantum computing.");

Ollama (Local Models)

using Microsoft.Agents.AI.Ollama;

var client = new OllamaClient(new Uri("http://localhost:11434"));
var agent = client.AsAIAgent(
    model: "llama3.2",
    instructions: "You are a local AI assistant.");

var response = await agent.RunAsync("Summarize this document.");

Generic IChatClient Agent

Any service implementing Microsoft.Extensions.AI.IChatClient can be used with ChatClientAgent:

using Microsoft.Agents.AI;

// IChatClient from any provider
IChatClient chatClient = GetChatClientFromAnyProvider();

AIAgent agent = new ChatClientAgent(
    chatClient,
    instructions: "You are a helpful assistant.");

var response = await agent.RunAsync("Hello!");

---

Workflows

Workflows provide graph-based orchestration for multi-step tasks with type-safe routing, checkpointing, and human-in-the-loop support.

Sequential Workflow

using Microsoft.Agents.AI.Workflows;

var workflow = WorkflowBuilder.CreateSequential()
    .AddStep<ResearchStep>("research")
    .AddStep<WriteStep>("write")
    .AddStep<ReviewStep>("review")
    .Build();

var result = await workflow.ExecuteAsync(new ResearchInput { Topic = "AI in Healthcare" });

Conditional/Branching Workflow

var workflow = WorkflowBuilder.Create()
    .AddStep<AnalyzeIntentStep>("analyze")
    .AddBranch(
        condition: ctx => ctx.Get<Intent>("intent") == Intent.Support,
        thenBranch: b => b.AddStep<HandleSupportStep>("support"),
        elseBranch: b => b.AddStep<HandleSalesStep>("sales"))
    .Build();

Agent Group Chat

Multiple agents collaborating with termination conditions:

using Microsoft.Agents.AI.Workflows;

var analyst = new ChatClientAgent(chatClient,
    instructions: "You analyze data and provide insights. Be concise.");

var writer = new ChatClientAgent(chatClient,
    instructions: "You take analysis and write clear reports.");

var workflow = WorkflowBuilder.CreateGroupChat()
    .AddAgent(analyst, "analyst")
    .AddAgent(writer, "writer")
    .WithTerminationCondition(ctx =>
        ctx.Messages.Last().Content.Contains("[COMPLETE]"))
    .WithMaxIterations(10)
    .Build();

var result = await workflow.ExecuteAsync("Analyze Q4 sales and write a summary report.");

Handoff Pattern

Transfer control between specialized agents:

var triage = new ChatClientAgent(chatClient,
    instructions: "You triage requests and hand off to specialists.");

var billing = new ChatClientAgent(chatClient,
    instructions: "You handle billing questions.");

var technical = new ChatClientAgent(chatClient,
    instructions: "You handle technical support.");

var workflow = WorkflowBuilder.Create()
    .AddStep<AgentStep>("triage", triage)
    .AddHandoff(
        from: "triage",
        condition: ctx => ctx.Get<string>("department") == "billing",
        to: billing)
    .AddHandoff(
        from: "triage",
        condition: ctx => ctx.Get<string>("department") == "technical",
        to: technical)
    .Build();

Magentic Pattern (Lead Agent)

A lead agent directs other agents:

var orchestrator = new ChatClientAgent(chatClient,
    instructions: "You are an orchestrator. Delegate tasks to specialists and synthesize results.");

var workflow = WorkflowBuilder.CreateMagentic()
    .WithLeadAgent(orchestrator)
    .AddSpecialist("researcher", researchAgent, "Research specialist")
    .AddSpecialist("coder", codeAgent, "Code specialist")
    .Build();

var result = await workflow.ExecuteAsync("Build a web scraper that extracts product prices.");

---

Tools and Function Calling

Function Tools

public sealed class OrderTools
{
    [Function("get_order_status")]
    [Description("Retrieves the status of an order by ID")]
    public async Task<OrderStatus> GetOrderStatusAsync(
        [Description("The order ID to look up")] string orderId,
        CancellationToken ct = default)
    {
        // Implementation
        return await _orderService.GetStatusAsync(orderId, ct);
    }

    [Function("cancel_order")]
    [Description("Cancels an order if eligible")]
    public async Task<CancelResult> CancelOrderAsync(
        [Description("The order ID to cancel")] string orderId,
        CancellationToken ct = default)
    {
        // Implementation
        return await _orderService.CancelAsync(orderId, ct);
    }
}

// Register tools with agent
var agent = chatClient.AsAIAgent(
    instructions: "You help customers with their orders.",
    tools: new OrderTools());

MCP (Model Context Protocol) Tools

Connect to MCP servers for external tools:

using Microsoft.Agents.AI.Tools.MCP;

// Connect to an MCP server
var mcpClient = new MCPClient("https://api.example.com/mcp");

// Get available tools from the server
var tools = await mcpClient.ListToolsAsync();

// Create agent with MCP tools
var agent = chatClient.AsAIAgent(
    instructions: "You have access to external data sources via MCP.",
    tools: tools);

// Or connect to hosted MCP servers (Azure OpenAI, OpenAI Responses API)
var responsesClient = client.GetResponsesClient("gpt-4o");
var agent = responsesClient.AsAIAgent(
    instructions: "You have access to hosted tools.",
    hostedTools: ["web_search", "file_search", "code_interpreter"]);

Hosted Tools (Responses API)

using Microsoft.Agents.AI.OpenAI;

var responsesClient = client.GetResponsesClient("gpt-4o");

var agent = responsesClient.AsAIAgent(
    instructions: "You are a research assistant.",
    hostedTools: new HostedTools
    {
        WebSearch = new WebSearchTool(),
        FileSearch = new FileSearchTool { VectorStoreIds = ["vs_123"] },
        CodeInterpreter = new CodeInterpreterTool()
    });

var response = await agent.RunAsync("Search for recent papers on climate change and analyze the data.");

---

Chat History and Sessions

Session Management

using Microsoft.Agents.AI.Sessions;

// Create a new session
var session = new AgentSession();

// Multi-turn conversation
await agent.RunAsync("What's the weather?", session);
await agent.RunAsync("Will it rain tomorrow?", session); // Has context from previous turn

// Persist session for later
var sessionData = session.Serialize();
// ... save to database ...

// Restore session later
var restoredSession = AgentSession.Deserialize(sessionData);

Chat History Providers

Use Redis or other providers for distributed session storage:

using Microsoft.Agents.AI.Sessions.Redis;

builder.Services.AddRedisChatHistoryProvider(
    connectionString: "localhost:6379");

// Agent automatically uses Redis for session persistence
var agent = chatClient.AsAIAgent(
    instructions: "You remember previous conversations.",
    chatHistoryProvider: provider);

Custom Context Providers

public class RAGContextProvider : IContextProvider
{
    private readonly IVectorStore _vectorStore;

    public async Task<IEnumerable<ChatMessage>> GetContextAsync(
        string userMessage,
        CancellationToken ct)
    {
        // Retrieve relevant documents
        var embedding = await GenerateEmbeddingAsync(userMessage, ct);
        var docs = await _vectorStore.SearchAsync(embedding, top: 5, ct);

        return docs.Select(d => new ChatMessage(
            Role.System,
            $"Context: {d.Content}"));
    }
}

var agent = chatClient.AsAIAgent(
    instructions: "You answer based on the provided context.",
    contextProviders: [new RAGContextProvider(vectorStore)]);

---

Middleware

Middleware intercepts agent actions for logging, authorization, rate limiting, and modification.

Authorization Middleware

public class AuthorizationMiddleware : IAgentMiddleware
{
    public async Task<AgentResponse> InvokeAsync(
        AgentContext context,
        Func<AgentContext, Task<AgentResponse>> next)
    {
        // Check authorization before processing
        if (context.FunctionName == "cancel_order")
        {
            var userId = context.User.Identity?.Name;
            var orderId = context.Arguments["orderId"]?.ToString();

            if (!await _authService.CanCancelOrderAsync(userId, orderId))
            {
                return new AgentResponse("You are not authorized to cancel this order.");
            }
        }

        return await next(context);
    }
}

// Register middleware
var agent = chatClient.AsAIAgent(instructions: "...")
    .UseMiddleware<AuthorizationMiddleware>();

Logging Middleware

public class LoggingMiddleware : IAgentMiddleware
{
    private readonly ILogger<LoggingMiddleware> _logger;

    public async Task<AgentResponse> InvokeAsync(
        AgentContext context,
        Func<AgentContext, Task<AgentResponse>> next)
    {
        _logger.LogInformation(
            "Agent {AgentName} processing: {Message}",
            context.AgentName,
            context.UserMessage);

        var stopwatch = Stopwatch.StartNew();
        var response = await next(context);
        stopwatch.Stop();

        _logger.LogInformation(
            "Agent {AgentName} completed in {ElapsedMs}ms",
            context.AgentName,
            stopwatch.ElapsedMilliseconds);

        return response;
    }
}

Rate Limiting Middleware

public class RateLimitMiddleware : IAgentMiddleware
{
    private readonly IRateLimiter _rateLimiter;

    public async Task<AgentResponse> InvokeAsync(
        AgentContext context,
        Func<AgentContext, Task<AgentResponse>> next)
    {
        var userId = context.User.Identity?.Name;

        if (!await _rateLimiter.TryAcquireAsync(userId))
        {
            return new AgentResponse("Rate limit exceeded. Please try again later.");
        }

        return await next(context);
    }
}

---

Enterprise Features

Observability with OpenTelemetry

using Microsoft.Agents.AI.Telemetry;

// Configure OpenTelemetry
builder.Services.AddOpenTelemetry()
    .WithTracing(tracing =>
    {
        tracing.AddAgentFrameworkInstrumentation();
        tracing.AddAzureMonitorTraceExporter();
    })
    .WithMetrics(metrics =>
    {
        metrics.AddAgentFrameworkMetrics();
        metrics.AddAzureMonitorMetricExporter();
    });

// All agent interactions automatically emit traces and metrics

Authentication with Microsoft Entra

using Azure.Identity;
using Microsoft.Agents.AI;

// Use managed identity in production
AzureOpenAIClient client = new AzureOpenAIClient(
    new Uri("https://<resource>.openai.azure.com"),
    new ManagedIdentityCredential());

// Or use Entra ID for user-delegated access
var credential = new InteractiveBrowserCredential();

Responsible AI

using Microsoft.Agents.AI.Safety;

var agent = chatClient.AsAIAgent(instructions: "...")
    .UseSafetyFilters(new SafetyOptions
    {
        // Prompt injection protection
        PromptInjectionDetection = true,

        // Content safety
        ContentSafety = new ContentSafetyOptions
        {
            HateSpeech = FilterSeverity.Medium,
            SelfHarm = FilterSeverity.High,
            Violence = FilterSeverity.Medium
        },

        // Task adherence monitoring
        TaskAdherenceMonitoring = true
    });

---

Streaming Responses

var agent = chatClient.AsAIAgent(instructions: "...");
var session = new AgentSession();

await foreach (var chunk in agent.RunStreamingAsync("Tell me a story.", session))
{
    Console.Write(chunk.Content);
}

---

Key Principles

• Prefer Agents for open-ended tasks -- Use agents when the task is conversational or requires autonomous tool use. If you can write a deterministic function, do that instead.
• Use Workflows for structured processes -- When the process has well-defined steps or requires explicit control over execution order, use workflows over single agents.
• Choose the right API -- Chat Completion for simple cases, Responses API for hosted tools, Assistants for persistent stateful conversations.
• Design focused tools -- Each tool should represent a single domain. Use [Description] attributes so the model knows when and how to call them.
• Do not store API keys in code -- Use environment variables, Azure Key Vault, or Managed Identity (see [skill:dotnet-csharp-configuration])
• Implement middleware for cross-cutting concerns -- Use middleware for logging, authorization, and rate limiting rather than duplicating logic in tools.
• Use chat history providers for distributed apps -- In multi-instance deployments, use Redis or other providers for session persistence.
• Enable observability in production -- Always configure OpenTelemetry tracing and metrics for production deployments.
• Validate inputs in tools -- AI models may call tools with unexpected arguments. Validate all inputs before executing operations.
• Handle cancellation tokens -- Always propagate CancellationToken through tool methods to support timeouts and user cancellations.

---

Agent Gotchas

1. Do not hardcode API keys -- Use DefaultAzureCredential for development and ManagedIdentityCredential for production. Hardcoded secrets leak into source control.
2. Do not ignore function return types -- The model receives the serialized result. Return summary DTOs, not full entity graphs, to avoid exceeding token limits.
3. Do not create agents per request -- Agents are designed to be long-lived. Register in DI and reuse across requests.
4. Do not forget middleware ordering -- Middleware executes in registration order. Place authorization before logging if you want to log only authorized requests.
5. Do not assume MCP availability -- Always handle cases where MCP servers are unavailable or return errors.
6. Do not store sensitive data in chat history -- Filter out PII before persisting chat history. Use context providers for sensitive data instead.
7. Do not skip cancellation token propagation -- AI calls can hang or take too long. Always propagate CancellationToken to allow cancellation.
8. Do not mix streaming and non-streaming inconsistently -- Choose one pattern per endpoint to avoid confusing client behavior.

---

Prerequisites

• .NET 8.0 or later
• Microsoft.Agents.AI NuGet package (prerelease)
• Provider-specific packages (e.g., Microsoft.Agents.AI.OpenAI, Microsoft.Agents.AI.AzureAI)
• Azure subscription (for Azure OpenAI or Foundry)
• OpenAI API key (for OpenAI direct)
• Ollama installation (for local models)

---

References

• Microsoft Agent Framework documentation
• .NET AI ecosystem tools
• Agent Framework samples
• MCP Protocol
• Azure OpenAI documentation
• Microsoft Foundry Agent Service

Code Navigation (Serena MCP)

Primary approach: Use Serena symbol operations for efficient code navigation:

1. Find definitions: serena_find_symbol instead of text search
2. Understand structure: serena_get_symbols_overview for file organization
3. Track references: serena_find_referencing_symbols for impact analysis
4. Precise edits: serena_replace_symbol_body for clean modifications

When to use Serena vs traditional tools:

• Use Serena: Navigation, refactoring, dependency analysis, precise edits
• Use Read/Grep: Reading full files, pattern matching, simple text operations
• Fallback: If Serena unavailable, traditional tools work fine

Example workflow:

# Instead of:
Read: src/Services/OrderService.cs
Grep: "public void ProcessOrder"

# Use:
serena_find_symbol: "OrderService/ProcessOrder"
serena_get_symbols_overview: "src/Services/OrderService.cs"