Microsoft Agent Framework RC: Complete Guide to the Unified AutoGen + Semantic Kernel SDK for .NET and Python (2026)

If you’ve ever agonized over choosing between AutoGen and Semantic Kernel for your AI agent project, that decision just became irrelevant. On February 19, 2026, the Microsoft Agent Framework hit Release Candidate status, merging both frameworks into a single, unified SDK. The API surface is frozen, GA is targeted for Q1 2026, and everything you build against it today will work when it ships.

What Is the Microsoft Agent Framework and Why Did Microsoft Merge Everything?

For the past two years, Microsoft’s AI agent ecosystem was split down the middle. Semantic Kernel brought enterprise-grade planning, tool integration, middleware pipelines, and telemetry. AutoGen delivered flexible multi-agent conversation management and elegant agent abstractions that made experimentation a breeze. The problem? Developers who prototyped with AutoGen often had to rewrite everything in Semantic Kernel when it was time to move to production.

The Microsoft Agent Framework resolves this split entirely. It combines AutoGen’s intuitive agent abstractions with Semantic Kernel’s enterprise features — session-based state management, type safety, middleware, and telemetry — and adds graph-based workflows for explicit multi-agent orchestration. It’s fully open-source on GitHub, and delivers a consistent programming model across both .NET and Python.

As the official Microsoft Foundry blog puts it, teams no longer have to choose between experimentation and production — the same framework handles both.

5 Core Features of the Microsoft Agent Framework

1. Agent Creation in a Few Lines of Code

The barrier to entry is remarkably low. With Python, a single pip install and three to four lines of code give you a fully functional agent. No sprawling boilerplate, no configuration nightmares — just straight to the logic that matters.

# Python - Microsoft Agent Framework Quick Start
pip install agent-framework --pre

from agent_framework import OpenAIChatClient

# Create an agent in 3 lines
agent = OpenAIChatClient().as_agent(
    instructions="You are a helpful coding assistant.",
    default_options={"temperature": 0.7, "max_tokens": 1000}
)

# Run it
result = await agent.run("Explain how to build a REST API in Python")
print(result)

For .NET developers, the experience is equally streamlined. Add the Microsoft.Agents.AI.OpenAI NuGet package, wire up your Azure OpenAI credentials, and you’re building agents with C#’s strong type system backing every tool call and response.

2. Type-Safe Function Tools

Function tools let your agents call external code — database queries, API calls, file operations — with type-safe definitions. In .NET, this leverages C#’s type system natively. In Python, type hints provide the same guardrails. This isn’t just syntactic sugar; it means runtime errors from malformed tool calls are caught at definition time, not in production at 3 AM.

3. Graph-Based Multi-Agent Workflows

This is where the Microsoft Agent Framework truly differentiates itself. You can compose agents and functions into four orchestration patterns, each with built-in streaming, checkpointing, and human-in-the-loop support:

• Sequential: A pipeline where agents hand off work step by step — perfect for content pipelines or data processing chains
• Concurrent: Multiple agents work in parallel and aggregate results — ideal for research tasks or multi-source analysis
• Handoff: Control transfers between agents based on context — think customer support routing where a billing question automatically routes to the billing specialist agent
• Group Chat: An orchestrator coordinates who speaks next, enabling agents to review and build on each other’s responses across multiple rounds with shared context

The checkpointing capability deserves special attention. Long-running workflows can save state on the server side, recover from interruptions, and resume exactly where they left off. For production systems that process hundreds of tasks daily, this is the difference between “it works in demo” and “it works at scale.”

4. Multi-Provider Support — No Vendor Lock-in

The framework supports Azure OpenAI, OpenAI, GitHub Copilot, Anthropic Claude, AWS Bedrock, Ollama, and more. One codebase, swap the provider. This makes cost optimization, model benchmarking, and compliance requirements dramatically easier to handle. You’re not married to a single LLM provider, and switching is a configuration change rather than a rewrite.

5. Interoperability via A2A, AG-UI, and MCP Standards

The Microsoft Agent Framework supports Agent-to-Agent (A2A), AG-UI, and Model Context Protocol (MCP) standards out of the box. This means your agents can interoperate with agents built in other frameworks — a clear signal that Microsoft is building toward an open agentic ecosystem rather than a walled garden.

Hands-On: Building a Multi-Agent Handoff Workflow

Theory is great, but let’s build something. Here’s a practical example of a handoff orchestration pattern — a customer inquiry system that classifies incoming requests and routes them to specialized agents.

# Multi-Agent Handoff Example
from agent_framework import OpenAIChatClient, Workflow
from agent_framework.orchestrations import HandoffOrchestration

# Define specialized agents
client = OpenAIChatClient()

triage_agent = client.as_agent(
    name="TriageAgent",
    instructions="Classify customer inquiries and route to the appropriate specialist."
)

billing_agent = client.as_agent(
    name="BillingAgent",
    instructions="Handle payment, refund, and subscription-related inquiries."
)

tech_agent = client.as_agent(
    name="TechSupportAgent",
    instructions="Provide technical troubleshooting and problem resolution."
)

# Configure handoff orchestration
orchestration = HandoffOrchestration(
    agents=[triage_agent, billing_agent, tech_agent],
    default_agent=triage_agent
)

# Run with streaming
async for chunk in orchestration.run(
    "My subscription didn't renew but I was still charged",
    stream=True
):
    print(chunk, end="")

Notice how the HandoffOrchestration handles the routing logic. The triage agent analyzes the inquiry, recognizes it involves both billing and technical aspects, and hands off to the billing agent first. If the billing agent determines there’s a technical issue underneath, it can further hand off to the tech support agent. All of this happens within a single streaming response.

The same pattern works in C#/.NET with the NuGet package. If your team runs a full-stack C# operation, being able to define agent workflows in the same language as your backend is a major productivity win.

Microsoft Agent Framework vs. LangChain vs. CrewAI: Where Each Shines

Let’s be real — the Microsoft Agent Framework isn’t the only game in town. Here’s how it stacks up against the competition:

LangChain / LangGraph offers 600+ integrations and the largest ecosystem of any agent framework. If you need to connect to an obscure data source or niche tool, LangChain probably has a connector. The trade-off is complexity — the abstraction layers can become unwieldy for simple use cases, and the learning curve is steeper than it needs to be.

CrewAI excels at role-based agent collaboration with a clean, intuitive API. For rapid prototyping or small-to-medium scale agent setups, CrewAI’s simplicity is hard to beat. It’s Python-only, though, which limits its reach in enterprise environments.

The Microsoft Agent Framework differentiates on three fronts. First, it’s the only major framework offering consistent APIs across both .NET and Python — most competitors are Python-first or Python-only. Second, native Azure and Microsoft ecosystem integration (Azure AI, Microsoft 365, GitHub Copilot) is unmatched. Third, the enterprise-grade observability inherited from Semantic Kernel — built-in OpenTelemetry, middleware pipelines, structured logging — makes it production-ready from day one.

Where the Microsoft Agent Framework falls short: LangChain still wins on breadth of third-party integrations, and CrewAI remains more approachable for teams that just want to get something running quickly without learning orchestration patterns.

Migrating from AutoGen or Semantic Kernel

Already running AutoGen or Semantic Kernel in production? Microsoft has published a comprehensive migration guide that maps existing constructs to their Agent Framework equivalents. Since we’re at RC, the API surface is frozen — migrating now means your code won’t break when GA lands.

The key mappings: Semantic Kernel plugins and functions become Function Tools. AutoGen’s ConversableAgent maps to the unified Agent abstraction. Existing prompt templates and connectors are largely reusable — the main effort goes into converting orchestration logic to the new graph-based workflow system. For most projects, this is a weekend’s worth of work, not a multi-sprint rewrite.

Why You Should Start Building Now

RC doesn’t mean “almost done” — it means the API is locked. Code you write today will run unchanged after GA ships. According to InfoQ’s analysis, enterprise adoption is expected to accelerate rapidly once GA drops. Starting now means you can deploy to production the same week GA is announced.

Head to the official documentation and work through the Quick Start guide to build your first agent. Then explore the samples repository for production patterns including RAG implementations, group chat workflows, and multi-provider setups. The unified SDK that the AI agent community has been waiting for is here — and the window to get ahead of the adoption curve is right now.

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