If you’ve been following the Agent Framework journey from the early Semantic Kernel and AutoGen days, this one is significant. Microsoft Agent Framework just hit version 1.0 — production-ready, stable APIs, long-term support commitment. It’s available for both .NET and Python, and it’s genuinely ready for real workloads.
Let me cut through the announcement noise and focus on what matters if you’re building AI-powered apps with .NET.
The short version
Agent Framework 1.0 unifies what used to be Semantic Kernel and AutoGen into a single, open-source SDK. One agent abstraction. One orchestration engine. Multiple AI providers. If you’ve been bouncing between Semantic Kernel for enterprise patterns and AutoGen for research-grade multi-agent workflows, you can stop. This is the one SDK now.
Getting started is almost unfairly simple
Here’s a working agent in .NET:
// dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Foundry;
using Azure.Identity;
var agent = new AIProjectClient(endpoint: "https://your-project.services.ai.azure.com")
.GetResponsesClient("gpt-5.3")
.AsAIAgent(
name: "HaikuBot",
instructions: "You are an upbeat assistant that writes beautifully."
);
Console.WriteLine(await agent.RunAsync("Write a haiku about shipping 1.0."));
That’s it. A handful of lines and you have an AI agent running against Azure Foundry. The Python equivalent is equally concise. Add function tools, multi-turn conversations, and streaming as you go — the API surface scales up without getting weird.
Multi-agent orchestration — this is the real deal
Single agents are fine for demos, but production scenarios usually need coordination. Agent Framework 1.0 ships with battle-tested orchestration patterns straight from Microsoft Research and AutoGen:
- Sequential — agents process in order (writer → reviewer → editor)
- Concurrent — fan out to multiple agents in parallel, converge results
- Handoff — one agent delegates to another based on intent
- Group chat — multiple agents discuss and converge on a solution
- Magentic-One — the research-grade multi-agent pattern from MSR
All of them support streaming, checkpointing, human-in-the-loop approvals, and pause/resume. The checkpointing part is crucial — long-running workflows survive process restarts. For us .NET developers who’ve built durable workflows with Azure Functions, this feels familiar.
The features that matter most
Here’s my shortlist of what’s worth knowing:
Middleware hooks. You know how ASP.NET Core has middleware pipelines? Same concept, but for agent execution. Intercept every stage — add content safety, logging, compliance policies — without touching agent prompts. This is how you make agents enterprise-ready.
Pluggable memory. Conversational history, persistent key-value state, vector-based retrieval. Choose your backend: Foundry Agent Service, Mem0, Redis, Neo4j, or roll your own. Memory is what turns a stateless LLM call into an agent that actually remembers context.
Declarative YAML agents. Define your agent’s instructions, tools, memory, and orchestration topology in version-controlled YAML files. Load and run with a single API call. This is a game-changer for teams that want to iterate on agent behavior without redeploying code.
A2A and MCP support. MCP (Model Context Protocol) lets agents discover and invoke external tools dynamically. A2A (Agent-to-Agent protocol) enables cross-runtime collaboration — your .NET agents can coordinate with agents running in other frameworks. A2A 1.0 support is coming soon.
The preview features worth watching
Some features shipped as preview in 1.0 — functional but APIs may evolve:
- DevUI — a browser-based local debugger for visualizing agent execution, message flows, and tool calls in real time. Think Application Insights, but for agent reasoning.
- GitHub Copilot SDK and Claude Code SDK — use Copilot or Claude as an agent harness directly from your orchestration code. Compose a coding-capable agent alongside your other agents in the same workflow.
- Agent Harness — a customizable local runtime giving agents access to shell, file system, and messaging loops. Think coding agents and automation patterns.
- Skills — reusable domain capability packages that give agents structured capabilities out of the box.
Migrating from Semantic Kernel or AutoGen
If you have existing Semantic Kernel or AutoGen code, there are dedicated migration assistants that analyze your code and generate step-by-step migration plans. The Semantic Kernel migration guide and AutoGen migration guide walk you through everything.
If you’ve been on the RC packages, upgrading to 1.0 is just a version bump.
Wrapping up
Agent Framework 1.0 is the production milestone that enterprise teams have been waiting for. Stable APIs, multi-provider support, orchestration patterns that actually work at scale, and migration paths from both Semantic Kernel and AutoGen.
The framework is fully open source on GitHub, and you can get started today with dotnet add package Microsoft.Agents.AI. Check out the quickstart guide and the samples to get your hands dirty.
If you’ve been waiting for the “safe to use in production” signal — this is it.
