Microsoft Agent Framework (MAF)

Overview

Microsoft Agent Framework (MAF) is the official successor to AutoGen, designed for building production-grade AI agents and multi-agent workflows. Released in 2025, MAF represents Microsoft's vision for the next generation of agentic AI development — combining the research-backed foundations of AutoGen with enterprise-ready features for production deployment.

The framework provides a comprehensive set of tools, abstractions, and integrations for developing AI agents that can reason, plan, and execute tasks across Microsoft's ecosystem and beyond. With native support for both Python and .NET, MAF targets developers building enterprise AI applications who need reliability, observability, and scalability.

MAF is built on three core pillars: Agents (the building blocks), Workflows (how agents orchestrate), and Integrations (how agents connect to the world).

Features

Agent Capabilities

• Sequential Workflows: Chain multiple agents together in a defined order, where each agent's output becomes the next agent's input. Ideal for linear processes like data processing pipelines.

• Concurrent Workflows: Run multiple agents in parallel, aggregating results when all complete. Perfect for tasks that can be split into independent sub-tasks.

• Middleware Support: Insert custom processing logic between agent steps for logging, validation, transformation, or routing.

• Declarative Agent Definitions: Define agents using YAML configuration files for easy version control and team collaboration.

• Multi-LLM Provider Support: Works with OpenAI, Azure OpenAI, and supports custom provider integrations.

Observability & Production Features

• OpenTelemetry Integration: Built-in tracing for monitoring agent execution, latency, and costs across distributed systems.

• Agent-to-Agent (A2A) Protocol: Standardized protocol for agents to communicate and delegate tasks to each other.

• Azure Integration: Native support for Azure Functions, Durable Task Framework, and other Azure services for scalable deployment.

Migration Support

• AutoGen Migration Guide: Comprehensive guide for migrating existing AutoGen projects to MAF.

• Semantic Kernel Migration Guide: Path for teams using Semantic Kernel to transition to MAF.

Installation

Python

pip install agent-framework

.NET

dotnet add package Microsoft.Agents.AI

Quick Start

Python Example

from agent_framework import Agent, Workflow

# Define agents
researcher = Agent(
    name="researcher",
    instructions="You are a research assistant. Gather information from the web.",
    model="gpt-4o"
)

analyst = Agent(
    name="analyst", 
    instructions="You are a data analyst. Analyze and synthesize research findings.",
    model="gpt-4o"
)

# Create sequential workflow
workflow = Workflow(
    steps=[researcher, analyst],
    name="research_pipeline"
)

# Run the workflow
result = workflow.run("Research the latest trends in AI agent frameworks")
print(result.final_output)

.NET Example

using Microsoft.Agents.AI;

var researcher = new Agent("researcher", "You are a research assistant.", "gpt-4o");
var analyst = new Agent("analyst", "You are a data analyst.", "gpt-4o");

var workflow = new WorkflowBuilder()
    .AddStep(researcher)
    .AddStep(analyst)
    .Build("research_pipeline");

var result = await workflow.RunAsync("Research AI agent frameworks");
Console.WriteLine(result.FinalOutput);

Core Concepts

Agents

Agents are the fundamental building blocks in MAF. Each agent has:

• Name: Unique identifier for the agent
• Instructions: System prompt defining the agent's role and behavior
• Model: LLM provider and model to use
• Tools: Optional tools the agent can invoke

Workflows

Workflows define how agents collaborate:

• Sequential: Linear execution where each agent waits for the previous
• Concurrent: Parallel execution with result aggregation
• Custom: Define your own orchestration logic

Integrations

MAF provides integrations with:

• Azure Functions: Deploy agents as serverless functions
• Durable Task Framework: Long-running workflows with state persistence
• OpenTelemetry: Distributed tracing and monitoring

Advanced Features

Middleware

Add custom processing between agent steps:

from agent_framework import Middleware

class LoggingMiddleware(Middleware):
    def process(self, step, input_data):
        print(f"Step {step.name} starting...")
        result = step.execute(input_data)
        print(f"Step {step.name} completed.")
        return result

workflow = Workflow(
    steps=[researcher, analyst],
    middleware=[LoggingMiddleware()]
)

Declarative Configuration

Define agents and workflows in YAML:

agents:
  researcher:
    instructions: "You are a research assistant."
    model: gpt-4o
    
  analyst:
    instructions: "You are a data analyst."
    model: gpt-4o

workflows:
  research_pipeline:
    type: sequential
    steps:
      - researcher
      - analyst

Pros

• ✅ Official Microsoft backing with enterprise support
• ✅ Production-ready with observability built-in
• ✅ Excellent .NET and Python support
• ✅ Deep Azure ecosystem integration
• ✅ Clear migration path from AutoGen
• ✅ OpenTelemetry integration for monitoring

Cons

• ❌ Newer framework with smaller community
• ❌ Azure-centric design may limit non-Azure users
• ❌ Less mature than LangChain/CrewAI
• ❌ Documentation still evolving

When to Use

Choose Microsoft Agent Framework when:

• Building enterprise AI applications on Azure
• Need production-grade observability and monitoring
• Working with .NET/Python stack
• Migrating from AutoGen or Semantic Kernel
• Require long-running workflows with state persistence

Consider alternatives when:

• Building simple scripts or prototypes (CrewAI may be faster)
• Need extensive third-party integrations (LangChain has more)
• Working outside Microsoft ecosystem (LangGraph may be more flexible)

Use Cases

Use Case	Why Microsoft Agent Framework
Enterprise AI on Azure	Native Azure Functions and Durable Task integration
Multi-Agent Workflows	Sequential and concurrent agent orchestration
Production Observability	OpenTelemetry built-in for monitoring
.NET + Python Teams	First-class support for both ecosystems

Comparison with Alternatives

Feature	MAF	AutoGen	LangGraph	CrewAI
Paradigm	Workflow-based	Conversation	Graph-based	Role-based
Azure Native	✅ Yes	⚠️ Via integration	❌ No	❌ No
.NET Support	✅ Yes	⚠️ Limited	❌ No	❌ No
OpenTelemetry	✅ Built-in	❌ No	⚠️ Manual	❌ No
Declarative Config	✅ YAML	❌ No	❌ No	❌ No
Learning Curve	Medium	Medium-High	Medium	Low
Best for	Enterprise Azure	Research	Custom graphs	Multi-agent teams

Best Practices

1. Define agents declaratively — Use YAML for version control and collaboration
2. Use sequential workflows — Start with linear pipelines before complex orchestration
3. Enable OpenTelemetry early — Built-in tracing for production monitoring
4. Leverage middleware — Add logging, validation between agent steps
5. Test workflows incrementally — Verify each agent step before full orchestration

Troubleshooting

Issue	Solution
Agent not executing	Verify model configuration and API keys
Workflow fails silently	Enable OpenTelemetry tracing for debugging
Middleware not running	Check middleware order in workflow definition
Azure deployment fails	Verify Durable Task Framework configuration

Resources

• Official Documentation
• GitHub Repository
• Python Samples
• .NET Samples
• AutoGen Migration Guide