Microsoft Agent Framework is the current Microsoft stack for code-first AI agents and multi-agent workflows in Python and .NET. It combines agent abstractions with explicit workflow graphs and supports interoperability such as MCP and A2A.
Key takeaways
- Designed for both individual agents and durable multi-agent workflows.
- Supports Python and .NET with Microsoft cloud integrations but is not limited to one model provider.
- Microsoft provides migration guidance for AutoGen and Semantic Kernel users.
What is Microsoft Agent Framework?
Microsoft framework for building Python and .NET agents, graph-based workflows and multi-agent systems with tools, state, human approval and protocol integrations. It is a strong candidate for teams that want one Microsoft-supported path spanning simple agents and explicit orchestration, especially where Python and .NET need to coexist.
What can you build with Microsoft Agent Framework?
- Create tool-using agents with structured responses and streaming.
- Compose agents and deterministic functions in graph workflows.
- Pause for human input, checkpoint state and resume long-running work.
- Connect tools and remote agents through MCP and A2A integrations.
These are documented capabilities, not a guarantee that every model, provider or deployment supports the same behavior. Validate the exact SDK version, model features and tool permissions in a disposable environment before moving a workflow into production.
What is a sensible first project?
Build a read-only support triage workflow: one agent classifies a ticket, a deterministic node retrieves approved knowledge and a final node drafts a response for human review. Measure routing accuracy, unsupported answers and resume behavior after an intentional interruption.
Keep the first run narrow and observable: one input, a small tool allowlist, explicit success criteria, a cost ceiling and a human review point before any external write. Save the prompt, model, SDK version, tool arguments and final result so the test can be reproduced.
How does the architecture handle state and tools?
The framework separates agents from workflows. Agents manage model interaction and tools, while graph nodes and edges make routing, parallel work, checkpoints and human-in-the-loop transitions explicit. State may include conversation history and workflow data, depending on the chosen persistence layer.
Treat model output as untrusted input. Validate structured data, set timeouts and iteration limits, make write operations idempotent where possible, and separate read-only discovery from actions that modify files, infrastructure, customer records or messages.
What should you review before deployment?
- Restrict MCP and A2A endpoints to approved services.
- Review cloud identity scopes separately from model credentials.
- Persist checkpoints only in stores that meet the data classification of the workflow.
Use least-privileged credentials and isolate code execution, browsers and shell tools. Log tool calls without recording secrets, define an emergency stop, and test how the application behaves when the model, a tool or the network returns an error. Human approval should be enforced in application code for high-impact actions rather than requested only in a prompt.
What are the main limitations?
- The framework is evolving and migration work may be required from AutoGen or Semantic Kernel.
- Provider-specific features can still create portability gaps.
- Durable workflows add operational components beyond a basic agent loop.
This profile is based on public first-party documentation checked on 2026-10-04; Anavem did not run a comparative benchmark or a production deployment. APIs, package names, licensing boundaries and hosted services can change, so confirm the current documentation before adopting the framework.
Is Microsoft Agent Framework the right choice?
Choose it when its programming language, orchestration model and operational controls match a concrete workflow. Compare it with one simpler baseline, including a direct model API plus ordinary application code. The useful decision is not which framework has the longest feature list, but which one makes tool permissions, state, failure handling, evaluation and maintenance understandable to your team.