Microsoft Agent Framework is easy to misunderstand because it sits next to several other Microsoft names that teams already know: AutoGen, Semantic Kernel, Azure AI Foundry agents, Copilot Studio, and Microsoft Agent 365.
That is exactly why the core question matters: what is Microsoft Agent Framework actually supposed to be?
In 2026, the clearest answer is that Microsoft Agent Framework is Microsoft’s new open-source SDK and runtime for building, orchestrating, and deploying AI agents, and it is explicitly positioned as the direct successor to the agent concepts that grew up separately inside AutoGen and Semantic Kernel.
That positioning became much more concrete in early April 2026, when Microsoft announced Agent Framework 1.0 for both .NET and Python. At that point, the framework stopped looking like a promising consolidation effort and started looking like Microsoft’s production-ready foundation for agent development.
What Microsoft Agent Framework is
At a high level, Microsoft Agent Framework is a unified programming model for single agents, multi-agent systems, and workflow-style orchestration.
Microsoft’s own documentation is unusually direct here. The company says Agent Framework combines AutoGen’s simple agent abstractions with Semantic Kernel’s enterprise features such as state management, middleware, type safety, filters, and telemetry, then adds graph-based workflows for explicit multi-agent orchestration.
The official Microsoft Learn overview is even more explicit: Agent Framework is the direct successor to both AutoGen and Semantic Kernel. It combines AutoGen’s simpler abstractions for single-agent and multi-agent patterns with Semantic Kernel’s enterprise-focused features, then extends them with workflow and state-management capabilities intended for more operational use cases.
That matters because Microsoft had a real overlap problem. AutoGen became one of the best-known names in multi-agent experimentation. Semantic Kernel became a broader enterprise SDK with growing agent support. Both were useful, but the split created confusion for teams trying to choose a long-term stack.
Microsoft Agent Framework is the attempt to resolve that split.
Why Agent Framework matters now
The timing matters as much as the product definition.
Agent Framework was introduced on October 1, 2025 in public preview, and it reached version 1.0 on April 3, 2026 for both .NET and Python. Microsoft frames that release as production-ready, with stable APIs and long-term support. Its current overview documentation also describes the framework as the next generation of both Semantic Kernel and AutoGen, not as a side project.
For buyers and builders, that changes the question from “should we test this new Microsoft thing?” to “if we are building agents in the Microsoft ecosystem, should this now be our default starting point?”
For many teams, the answer is increasingly yes.
How Microsoft Agent Framework works
Agent Framework is built around a few ideas that are worth separating.
1. A unified agent abstraction
Older Microsoft stacks often made teams think in terms of provider-specific agent types. Agent Framework moves toward a more unified agent model, so teams can work with a cleaner abstraction instead of constantly switching mental models between providers and runtimes.
2. Workflows, not just chat loops
This is one of the biggest reasons the framework matters. Microsoft emphasizes workflows for sequential, concurrent, and branching execution rather than treating multi-agent systems as a pile of agent messages. That makes it easier to design systems with explicit control paths.
Microsoft describes workflows as graph-based systems that connect agents and functions for multi-step tasks with type-safe routing, checkpointing, and human-in-the-loop support.
That is a strong signal about where enterprise AI is going. The future is not only agent chats. It is structured, stateful, multi-step processes where agents participate inside a wider operational flow.
3. Enterprise middleware and state
Agent Framework inherits a more enterprise-oriented posture from Semantic Kernel. Middleware hooks, session-based state management, filters, telemetry, and long-running workflow support are central to the value proposition. This is a meaningful difference from frameworks that feel optimized mostly for experimentation.
4. Cross-provider model support
Microsoft says Agent Framework ships with first-party connectors for Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic Claude, Amazon Bedrock, Google Gemini, and Ollama. That is strategically important because it makes the framework less about locking teams into one model source and more about giving them a common operating layer.
Microsoft also highlights support across multiple providers, including Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic, Ollama, and others. That matters because enterprise teams increasingly want optionality rather than a framework locked to one model path.
5. Interoperability through A2A and MCP
Microsoft also pushes interoperability more aggressively than many buyers realize. Agent Framework 1.0 highlights cross-runtime interoperability via both A2A and MCP. In practice, that means Microsoft is not treating agent systems as a closed island. It is acknowledging that real enterprises will increasingly need agents, tools, and services from multiple vendors to work together.
Microsoft says Agent Framework can integrate any API via OpenAPI, connect dynamically to tools through MCP, and collaborate across runtimes with Agent2Agent. That means the framework is positioned to work inside a broader interoperability stack rather than forcing everything through a proprietary control surface.
Microsoft Agent Framework and enterprise AI
For enterprise teams, the important part is not just that Microsoft launched another SDK. It is that Microsoft is trying to converge earlier agent tooling into a more unified, production-oriented stack.
If you have followed Microsoft’s agent story across AutoGen, Semantic Kernel, Azure AI Foundry, Foundry Agent Service, and governance tooling, Agent Framework is the connective tissue. It is designed to give developers one place to build agents, orchestrate workflows, connect tools, and move into production with observability and enterprise controls in view from the start.
Microsoft’s launch positioned the framework as a way to experiment locally and then deploy into Azure AI Foundry, use built-in observability, durability, and compliance patterns, connect to APIs through OpenAPI, work with Agent2Agent (A2A) and Model Context Protocol (MCP), and orchestrate multi-agent systems with workflows rather than only loose agent loops.
Azure AI Foundry is also central to the story. Microsoft’s launch tied Agent Framework closely to Foundry’s observability, governance, and managed services. In other words, Agent Framework is the developer-side abstraction, while Foundry becomes a key operational environment around it.
Microsoft Agent Framework vs AutoGen and Semantic Kernel
This is the comparison most teams actually care about.
AutoGen
AutoGen helped popularize multi-agent patterns and easy agent-to-agent composition. It remains historically important, but Microsoft now treats Agent Framework as the successor path for teams that want those patterns in a more production-oriented system.
Semantic Kernel
Semantic Kernel still matters as a broader SDK and ecosystem, but Microsoft’s migration guidance makes the direction clear for agent-centric development. If your main job is building and orchestrating agents, Agent Framework is where Microsoft wants that work to land.
In simple terms: AutoGen was strong on agent orchestration ideas, Semantic Kernel was strong on enterprise SDK structure, and Microsoft Agent Framework is the product Microsoft built after deciding those strengths should stop living in separate places.
The workflow point matters a lot. Many agent systems break because the line between open-ended reasoning and deterministic process control stays blurry. Agent Framework tries to make that line more explicit. Use agents where autonomy is useful. Use workflows where execution order, routing, and state need to be controlled.
Microsoft’s documentation even says the quiet part out loud: if a task can simply be handled by a normal function, do that instead of using an AI agent. That is a healthy design principle, and it makes the framework feel more grounded than a pure “everything should be agentic” pitch.
Microsoft Agent Framework vs Microsoft Agent 365
This is another important distinction.
Microsoft Agent Framework is a developer framework. Microsoft Agent 365 is an enterprise control-plane and governance product. They are related to the same agent market, but they do different jobs.
If your team is writing code, orchestrating workflows, integrating tools, and choosing models, Agent Framework is the relevant layer. If your company is trying to govern agents across the organization, handle enterprise policies, and manage agent deployment at scale, Agent 365 is closer to that buying decision.
Many companies will eventually care about both, but they should not be confused as the same product.
Who should care most
Microsoft-centric enterprise teams
If your organization is already invested in Azure, Foundry, Microsoft 365, or broader Microsoft governance tooling, Agent Framework is one of the most relevant agent development paths to evaluate.
Teams building multi-agent business workflows
If the goal is not just a chatbot but a system that coordinates multiple agents and services across steps, approvals, retries, and human review, Agent Framework’s workflow model becomes especially attractive.
Developers who want a more operational framework
Some frameworks are excellent for fast experimentation but weaker at bridging to governed enterprise deployment. Microsoft is clearly trying to make Agent Framework stronger on that bridge.
When Microsoft Agent Framework is the right choice
Agent Framework is strongest when:
- Your team already builds on .NET or Python and wants a supported Microsoft path.
- You need multi-agent orchestration with clearer execution control than free-form autonomous loops.
- You care about middleware, telemetry, state, checkpointing, and long-running workflows.
- You want a model-agnostic framework rather than a single-provider SDK.
- You are migrating from AutoGen or Semantic Kernel and want the clearest forward path.
- You expect interoperability needs across MCP or A2A environments.
- You want to experiment locally and then deploy into Azure AI Foundry.
It is weaker when:
- You only need a lightweight prototype and do not want a fuller framework.
- Your team is deeply invested in another orchestration stack with no compelling reason to switch.
- You are looking for a no-code or business-user product rather than an SDK.
What teams should watch before committing
Even though Agent Framework 1.0 is now production-ready, teams should still evaluate it like an architecture decision, not a brand decision.
Look closely at how much explicit workflow control you want, which providers you actually plan to use, how important Microsoft-native tooling is to your organization, and whether your team wants a framework that can sit above several model sources. The framework is strongest when those needs are real. It is less compelling if you just want the thinnest possible wrapper around one provider’s API.
Teams should also look closely at preview maturity and roadmap stability, developer experience compared with OpenAI Agents SDK, Google ADK, LangGraph, and other options, how tightly the production story depends on Azure AI Foundry, and what governance, interoperability, and observability features are actually needed for the use case.
It is also worth testing how well the framework fits your organization’s human-in-the-loop, compliance, and deployment requirements. Microsoft clearly built Agent Framework for more governed production scenarios, so the payoff tends to rise as the system gets more complex.
The bottom line
Microsoft Agent Framework is Microsoft’s new center of gravity for agent development. It is not just another SDK sitting beside AutoGen and Semantic Kernel. Microsoft now positions it as the successor path that merges both lines into one production-ready framework.
For teams building multi-agent systems in 2026, that makes Microsoft Agent Framework one of the most important stacks to understand. If your organization wants explicit orchestration, enterprise-grade structure, cross-provider flexibility, and a clearer long-term Microsoft roadmap, it deserves a serious evaluation.
It matters because it represents a shift from fragmented agent experiments to a more unified enterprise framework for agents and workflows.
It is Microsoft’s clearest attempt yet to answer a big question facing enterprise AI teams: how do you build agent systems that are not only clever, but also structured, observable, interoperable, and deployable?
That does not make Agent Framework the default answer for every team. It does make it a product worth understanding, especially for organizations that expect multi-agent systems, workflow orchestration, and governance to become core parts of their software stack in 2026.