Getting Started with Microsoft Agent Framework

The Microsoft Agent Framework (MAF) is a .NET SDK for building multi-agent AI systems. Rather than a single “do everything” prompt, you compose specialized agents — each with their own tools and instructions — and let them hand off work to each other.

This tutorial walks you through building a simple two-agent system: a triage agent that routes requests and a resolver agent that handles them.

Prerequisites

.NET 8 or later
NuGet: Microsoft.AI.Agents.Core
An Azure OpenAI or OpenAI API key

Step 1: Install packages

dotnet new console -n AgentDemo
cd AgentDemo
dotnet add package Microsoft.AI.Agents.Core
dotnet add package Microsoft.SemanticKernel

Step 2: Define your first agent

Agents are defined with a name, instructions, and a set of tools (Semantic Kernel plugins):

using Microsoft.AI.Agents.Core;
using Microsoft.SemanticKernel;

var kernel = Kernel.CreateBuilder()
    .AddAzureOpenAIChatCompletion(
        deploymentName: "gpt-4o",
        endpoint: Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")!,
        apiKey: Environment.GetEnvironmentVariable("AZURE_OPENAI_KEY")!
    )
    .Build();

var triageAgent = new ChatCompletionAgent
{
    Name = "TriageAgent",
    Instructions = """
        You are a triage agent. Classify incoming requests as either:
        - 'billing' — questions about invoices, payments, or subscriptions
        - 'technical' — questions about bugs, features, or how the product works
        Then hand off to the appropriate agent.
        """,
    Kernel = kernel
};

Step 3: Define the resolver agent

var resolverAgent = new ChatCompletionAgent
{
    Name = "ResolverAgent",
    Instructions = """
        You are a support resolver. You receive pre-triaged requests and provide
        clear, concise answers. Always end with a confirmation question.
        """,
    Kernel = kernel
};

Step 4: Set up handoff topology

MAF’s HandoffBuilder lets you declare a routing graph between agents:

var chat = new AgentGroupChat(triageAgent, resolverAgent)
{
    ExecutionSettings = new AgentGroupChatSettings
    {
        TerminationStrategy = new ApprovalTerminationStrategy()
    }
};

Step 5: Run the workflow

chat.AddChatMessage(new ChatMessageContent(
    AuthorRole.User,
    "My invoice from last month looks wrong — I was charged twice."
));

await foreach (var response in chat.InvokeAsync())
{
    Console.WriteLine($"[{response.AuthorName}]: {response.Content}");
}

MAF will call the triage agent first, which classifies the issue as “billing” and hands off to the resolver agent. You’ll see each agent’s turn in the output.

What to explore next

Tool calling — give agents access to APIs, databases, or file systems via SK plugins
Human-in-the-loop — use ApprovalTerminationStrategy to pause for human confirmation before high-risk actions
AG-UI integration — stream agent events to a frontend using the AG-UI protocol for real-time visibility
Observability — connect to Azure Monitor or OpenTelemetry to trace agent decisions

The MAF documentation and samples on GitHub cover production patterns including state persistence and long-running workflows.