Microsoft Agent Framework Reaches Release Candidate — AutoGen + Semantic Kernel Unified

The fragmentation era of Microsoft’s agentic AI tooling is officially ending. On February 19, 2026, Microsoft announced that the Microsoft Agent Framework (MAF) has reached Release Candidate for both .NET and Python — unifying AutoGen and Semantic Kernel into a single, coherent open-source SDK. General Availability is targeted for Q1 2026.

If you’ve been running AutoGen or Semantic Kernel in production, the clock is ticking. AutoGen is entering maintenance-only mode.

What is the Microsoft Agent Framework?

MAF is Microsoft’s answer to the fragmentation problem it created itself. Over the past few years, Microsoft maintained two major agentic AI libraries:

  • AutoGen — a multi-agent conversation framework popular for its agent-loop and code execution patterns
  • Semantic Kernel — a more structured SDK with memory, planning, and plugin architecture

Both had overlapping use cases and incompatible APIs. Teams had to choose one and commit — or build awkward bridges between them.

MAF ends that. The new framework provides:

  • A unified agent abstraction that works the same way whether you’re writing .NET or Python
  • A2A (Agent-to-Agent) protocol support — the emerging standard for agent interoperability
  • MCP (Model Context Protocol) support — for structured tool and resource sharing between agents and models
  • A stable, versioned API surface (the RC announcement confirms API stability; v1.0 features are complete)

AutoGen is Entering Maintenance-Only Mode

This is the big news for AutoGen users. Microsoft is not planning major new features for AutoGen. It will receive security patches and critical bug fixes, but the development focus is entirely on MAF.

If you have significant AutoGen investments, start planning your migration now. Microsoft published a migration guide on the Semantic Kernel dev blog with the RC announcement.

The migration path covers:

  • Agent definition patterns
  • Tool/function calling
  • Multi-agent conversation loops
  • Memory and state management
  • Code execution sandboxing

What’s New in the Release Candidate

API Stability

The RC designation means the API surface is frozen. What you build against today won’t break when GA ships. This is critical for enterprise teams who need stability guarantees before committing to a framework.

A2A and MCP Protocol Support

MAF ships with native support for two emerging interoperability standards:

Agent-to-Agent (A2A): Allows agents built with different frameworks to communicate using a standard protocol. An agent built in MAF can receive tasks from, and send results to, agents built in OpenClaw, CrewAI, or any other A2A-compliant framework.

Model Context Protocol (MCP): Provides a structured way for agents to expose tools and resources to language models. Rather than custom function-calling implementations, MCP gives you a standard interface that any MCP-compatible model can use.

Together, these protocols position MAF as interoperability-first — something AutoGen never prioritized.

Migrating from AutoGen to Microsoft Agent Framework RC

Here’s a practical walkthrough of the migration for Python users.

Install MAF RC

pip install microsoft-agent-framework --pre
# Or for .NET:
dotnet add package Microsoft.AgentFramework --prerelease

AutoGen Agent Definition → MAF Agent Definition

Before (AutoGen):

from autogen import AssistantAgent, UserProxyAgent

assistant = AssistantAgent(
    name="assistant",
    llm_config={"model": "gpt-4o"},
)

user_proxy = UserProxyAgent(
    name="user_proxy",
    code_execution_config={"work_dir": "coding"},
)

user_proxy.initiate_chat(assistant, message="Write a hello world script")

After (MAF):

from microsoft_agent_framework import Agent, AgentRuntime, CodeExecutionTool

runtime = AgentRuntime()

assistant = Agent(
    name="assistant",
    model="gpt-4o",
    tools=[CodeExecutionTool(work_dir="coding")]
)

result = await runtime.run(
    agent=assistant,
    task="Write a hello world script"
)

The concepts are similar — agents, tools, runtime — but the API is cleaner and async-native.

Multi-Agent Patterns

MAF supports the same orchestrated multi-agent patterns AutoGen made popular, but with better support for structured handoffs:

from microsoft_agent_framework import AgentTeam, OrchestratorAgent

researcher = Agent(name="researcher", model="claude-sonnet-4-6", tools=[web_search])
writer = Agent(name="writer", model="gpt-4o")
editor = Agent(name="editor", model="claude-sonnet-4-6")

team = AgentTeam(
    orchestrator=OrchestratorAgent(),
    agents=[researcher, writer, editor]
)

result = await team.run("Write a comprehensive report on agentic AI trends in 2026")

A2A Integration

To expose your MAF agent to other frameworks via A2A:

from microsoft_agent_framework import A2AServer

server = A2AServer(agent=my_agent, port=8080)
await server.start()
# Your agent is now reachable by OpenClaw, CrewAI, etc. via the A2A protocol

Building Multi-Agent Pipelines with MAF

MAF’s orchestration model is opinionated in a useful way: workflow engines control sequencing; agents execute tasks. This is the same insight McKinsey QuantumBlack published in their agentic development framework — and MAF bakes it in architecturally.

A typical pipeline:

  1. Orchestrator receives the task and creates a plan (sequence of steps)
  2. Specialist agents (research, write, review, test, deploy) execute individual steps
  3. Orchestrator evaluates outputs and decides next steps
  4. Human review gates can be inserted at any step (MAF supports approval workflows)

This pattern scales from 2-agent pipelines to enterprise deployments with dozens of specialized agents.

Timeline

  • Now: Release Candidate — API stable, safe to build against
  • Q1 2026 (targeted): General Availability — production support, SLAs, LTS guarantees
  • Ongoing: AutoGen enters maintenance-only — security patches only

Sources

  1. Microsoft Developer Blogs — MAF Reaches Release Candidate
  2. Microsoft Developer Blogs — Semantic Kernel Migration Guide
  3. Microsoft Learn — MAF Documentation
  4. Visual Studio Magazine — MAF RC Coverage

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260223-2000

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