What Is the Microsoft Agent Framework (MAF)? The Open-Source Framework Bringing AI Agents from Prototype to Production

Building an AI assistant that answers questions is relatively straightforward. What's genuinely hard is making it reliably use tools, remember where it left off, collaborate with other AI agents, and keep running after an error or interruption.

The Microsoft Agent Framework, or MAF, is the open-source framework Microsoft built specifically for that stretch of the journey—from demo prototype to production system. It supports Python and .NET and can be used to build a single AI agent or orchestrate multiple agents working together on multi-step tasks.

MAF is not a new large language model, and it's not a chatbot you open in a browser. It's more like a set of developer tools and a runtime architecture that assembles models, tools, memory, workflows, safety controls, and monitoring capabilities into a coherent system.

If a large language model is the thinking brain, MAF is the shop floor management system: assigning tasks, enforcing process, keeping records, and pausing for human sign-off at critical steps.

The One-Line Summary

The Microsoft Agent Framework is an MIT-licensed open-source AI agent framework that merges AutoGen's multi-agent collaboration experience with Semantic Kernel's enterprise application foundation. It lets developers build observable, interruptible, and human-in-the-loop agent workflows in Python or .NET.

What Problem Does It Solve?

A standard chatbot works simply: receive a question, call a model, return an answer. But when AI must query databases, call external APIs, modify files, or run continuously for tens of minutes, systems need to handle much more.

• How do you break tasks down? Which steps should be model-driven, and which must follow fixed rules?
• How do you recover from failure? If the workflow is interrupted, can it resume from where it stopped rather than starting over?
• How do multiple agents collaborate? Should research, drafting, and review happen in sequence, in parallel, or handed off between agents?
• When should humans step in? How do you wait for human approval before sending an email, processing a payment, or modifying production data?
• How do you debug the system? Can developers see model calls, tool usage, and workflow decisions in action?

MAF packages these common requirements into a consistent programming interface and workflow component library. Teams don't need to rebuild an agent execution engine from scratch for every project.

Core Features

1. Supports Both Agents and Deterministic Workflows

Agents excel at work requiring judgment and flexibility, but their outputs can be unpredictable. Traditional workflows follow a predefined sequence and are more reliable. MAF allows you to mix both.

For example: an agent reads and interprets a customer complaint, a fixed workflow then queries order history and checks refund eligibility, and finally a human approves the resolution. Flexibility where it's needed; clear rules where compliance matters.

2. Built-In Multi-Agent Collaboration

When a task is too complex for one agent, you can deploy multiple specialized agents working in parallel. MAF provides coordination modes including sequential, parallel, handoff, and group conversation.

For example, generating a market report: one agent gathers data, one analyzes figures, and another checks citations. Developers define roles, available tools, and workflow boundaries—rather than letting models chat with each other indefinitely.

3. No Lock-In to a Single Model Provider

Official first-party connectors include Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic Claude, Amazon Bedrock, Google Gemini, and local Ollama.

This doesn't mean models are drop-in replacements for each other. Tool calling, context length, pricing, and safety mechanisms all differ. But a unified abstraction layer reduces the cost of switching or mixing models later.

4. Stateful Long-Running Tasks

MAF workflows support checkpoints, pausing, and resuming. The system saves progress so that even if a service restarts or the workflow is waiting for human approval, execution doesn't have to restart from step one.

This is especially valuable for tasks that span extended periods: customer escalations, document reviews, research analyses.

5. Observability and Control Built Into the Architecture

MAF integrates OpenTelemetry to record agent execution, model calls, and tool usage. Middleware lets you add logging, content inspection, exception handling, and compliance policies without rewriting core prompts.

It also supports human-in-the-loop workflows. When an agent is about to perform a high-risk action, it can pause and await approval—rather than relying on a warning phrase in a prompt to constrain the model.

6. Support for Open Protocols — MCP and A2A

MCP (Model Context Protocol) lets agents discover and use external tools. A2A (Agent-to-Agent) allows agents in different systems or frameworks to communicate using a standard protocol.

These integrations reduce the risk of being locked into a single platform. Note, though: connecting a protocol doesn't automatically mean it's secure. Permissions, data in transit, and third-party service data retention policies all still require review by the development team.

How Does MAF Differ from AutoGen and Semantic Kernel?

An important piece of context: MAF integrates capabilities accumulated by both AutoGen and Semantic Kernel. It isn't simply renaming one of those projects.

In short, MAF serves development teams that need high customization and want to control workflows through code. It's a different product category from lower-code options like Copilot Studio.

What Non-Engineers Need to Know

MAF's significance isn't that it makes AI suddenly smarter—it's that it makes it easier for enterprises to manage how AI works.

Many agent demos look smooth, but going into production means dealing with permissions, costs, error recovery, auditing, and accountability. MAF provides the engineering-level building blocks to put those control points inside the system.

That said, the framework can't decide for an enterprise which data it's appropriate to share with a model, and it doesn't automatically guarantee correct answers. Microsoft's own documentation reminds developers they must independently evaluate third-party systems, data flows, content filtering, security, and reliability.

Who Is It For?

• AI development teams using Python or .NET: Those who want to build single or multiple agents using a consistent conceptual model.
• Enterprises ready to take agent prototypes to production: Teams that need state persistence, monitoring, human approval workflows, and governance.
• Existing AutoGen or Semantic Kernel project teams: Those wanting to follow Microsoft's consolidated, primary framework roadmap.
• Systems that need to mix multiple models: Teams looking to reduce the degree to which a specific model provider is baked into the application architecture.

Who Might Not Need It?

• Those building a simple Q&A bot quickly: Calling a model API directly may be far simpler than adopting a full framework.
• Non-programmers: Visual tools like Copilot Studio offer a much gentler onramp.
• Workflows requiring only deterministic rule-based automation: If every step can be clearly defined with traditional code, adding LLMs and agents may be unnecessary overhead.
• Small teams lacking security and operations capabilities: Agents can call tools and take actions—which also means investing in permission isolation, testing, cost limits, and monitoring.

How to Get Started

MAF is MIT-licensed and open source on GitHub. Core packages for Python and .NET can be installed with:

# Python
pip install agent-framework

# .NET
dotnet add package Microsoft.Agents.AI

After installation, you'll still need to configure endpoints, credentials, or API keys for your chosen model service. If using Microsoft Foundry, you'll also need a corresponding Azure project and authentication.

Start with a single agent and a single tool. Confirm permissions and outputs before gradually adding memory, workflows, and multi-agent coordination. More agents don't automatically produce better results—but they do guarantee higher model call costs and harder debugging.

[!NOTE] MAF's core framework reached 1.0 in April 2026, but DevUI, some managed integrations, and newer features may still be at varying maturity levels. Before adopting in production, verify the documentation and package versions for each specific component—don't rely on the 1.0 label for the framework as a whole.

Our Take

MAF's most important contribution is that Microsoft has finally given AutoGen and Semantic Kernel's agent capabilities a clearer, unified direction. Previously, teams had to choose between "easy multi-agent experimentation" and "suitable for enterprise system integration." Microsoft now aims to cover both ends with a single framework.

Its strength is also its potential barrier. MAF spans models, tools, memory, workflows, protocols, observability, and cloud hosting—appropriate for complex systems, but potentially too much architectural overhead for smaller projects. Before adopting, ask: does this task genuinely require long-running execution, multi-role collaboration, or interrupt-and-resume capabilities?

A more pragmatic framing: treat MAF as a backend engineering framework for agent applications, not a button that automatically generates reliable AI employees. It provides guardrails and control points, but real reliability still comes from clear permission design, thorough testing, human review, and continuous monitoring.

Sources

• GitHub: Microsoft Agent Framework
• Official docs: Microsoft Agent Framework documentation
• 1.0 announcement: Microsoft Agent Framework Version 1.0
• Build 2026 update: Microsoft Agent Framework at BUILD 2026
• Accessed: 2026-07-20