Microsoft Agent Framework is Microsoft’s new framework for building AI agents and multi-agent workflows. 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.
That makes it one of the more important framework-level shifts in enterprise AI going into 2026.
What Microsoft Agent Framework is
Microsoft introduced Agent Framework on October 1, 2025 in public preview. In Microsoft’s own description, it is the open-source SDK and runtime that simplifies orchestration of multi-agent systems. The framework combines ideas from AutoGen and Semantic Kernel, then extends them with workflow and state-management capabilities intended for more operational use cases.
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 such as state management, type safety, filters, telemetry, and broad model support. It also adds graph-based workflows for explicit orchestration.
That is the clearest way to understand the product. Agent Framework is not just a fresh wrapper. It is Microsoft’s attempt to collapse multiple threads of its agent development story into one next-generation framework.
Why Microsoft created it
The problem Microsoft is addressing is familiar across the AI market: agent development got fragmented fast.
Research-driven frameworks were powerful but not always easy to operationalize. Enterprise-oriented frameworks offered stronger controls but sometimes felt heavier or less unified. Meanwhile, teams building multi-agent systems needed better observability, more explicit workflow control, and cleaner ways to move from local experimentation into managed cloud environments.
Agent Framework is aimed directly at that gap.
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)
- orchestrate multi-agent systems with workflows rather than only loose agent loops
That combination is why the framework matters. It sits at the intersection of developer tooling and enterprise operations.
What the framework includes
Microsoft frames Agent Framework around two primary capability categories: agents and workflows.
Agents
An agent in Agent Framework can use LLMs to process inputs, call tools and MCP servers, and generate responses. Microsoft’s documentation 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.
Workflows
Workflows are where the framework becomes more strategically interesting. Microsoft describes them as graph-based workflows 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.
State and long-running execution
Agent Framework is also designed for long-running and human-in-the-loop scenarios. This is important because many business processes do not finish in one turn. They branch, wait for approvals, retry failed actions, and maintain context over time. Microsoft is clearly building for that reality.
How Agent Framework fits with MCP, A2A, and Azure AI Foundry
One of the most useful parts of Microsoft’s design is that it does not treat agent development as a closed ecosystem.
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.
That is especially important for enterprise teams. Real environments are mixed. A company may use Azure AI Foundry for some workloads, OpenAI APIs for others, internal APIs for operational systems, and protocol-based connections for cross-agent or tool access. Agent Framework appears designed to sit in that reality, not pretend it does not exist.
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.
What makes Agent Framework different from older Microsoft agent tooling
The simplest answer is convergence.
AutoGen was influential for multi-agent experimentation and orchestration ideas. Semantic Kernel brought a more enterprise-oriented programming model and integration mindset. Agent Framework is Microsoft’s attempt to combine those strengths, reduce fragmentation, and make workflows a first-class part of the product.
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.
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.
Where teams should be cautious
Agent Framework is promising, but it is still important to evaluate it as a real engineering decision rather than a branding update.
Teams should 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
- what governance, interoperability, and observability features are actually needed for the use case
The right framework depends on the surrounding stack, not just on feature lists.
The practical takeaway
Microsoft Agent Framework 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.