GitHub – microsoft/agent-framework: A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.

Microsoft Agent Framework: Practical Multi‑Language Agents for Production

Introduction
This post explains what the Microsoft Agent Framework is and why it matters to enterprise teams.
The framework is a multi‑language toolkit for building, orchestrating and deploying AI agents in .NET and Python.
Why should you care?

• It provides repeatable architectures for agent workflows.
• It includes samples, templates and language bindings you can reuse.
• It surfaces operational and data governance requirements you must manage.

What the framework provides

The repository contains building blocks for everything from simple chat agents to graph‑based multi‑agent workflows.
You get sample projects, workflow templates and bindings for both Python and .NET.
That reduces ad hoc scripts and gives teams a consistent starting point.
Example snippet (conceptual Python):

from agent_framework import ResponsesAgent
agent = ResponsesAgent(provider="azure_openai", behavior="simple_chat")
agent.run()

Security and data governance

The project explicitly warns about third‑party servers and data egress risks.
Who owns retention and where data travels are organizational decisions.
What should you do first?

• Audit and log every external call an agent makes.
• Classify data before it leaves your boundary.
• Require human review gates for outputs used in decisions.

Operational patterns and adoption

Teams often prototype in Python and productionize stable flows in .NET.
Graph orchestration makes it easy to chain specialized agents into clear pipelines.
Think normalizer → classifier → summarizer.
That pattern reduces handoffs and clarifies ownership.
A quick anecdote: I recently reviewed a pilot that used this exact pattern and it cut manual work while making validation steps clearer.

Wrapping up
The Microsoft Agent Framework is a pragmatic option for teams that need repeatable, multi‑step AI services.
It accelerates delivery while increasing responsibility for data governance and output validation.
What should leaders ask next?
How will we validate outputs, control data flows and assign ownership for model changes?
Start with a focused pilot, enforce data boundaries and review the repository and examples here: https://github.com/microsoft/agent-framework.
That is how you move from experiment to operational GenAI with manageable risk.