How to Build AI Agents With Microsoft AutoGen

AutoGen in plain terms

Microsoft AutoGen is an open-source framework that builds AI agents which solve tasks by talking to each other. Instead of one model working alone, AutoGen sets up a conversation between agents — typically an assistant that proposes solutions and a user-proxy agent that can run code and feed back results — and the back-and-forth drives the work to completion. Its standout strength is letting agents write and execute code as part of the loop, which makes it powerful for data, analysis, and engineering tasks.

The mental model that unlocks AutoGen is conversation as control flow. Where other frameworks use an explicit loop or graph, AutoGen uses messages between agents. One agent suggests, another acts or critiques, and the dialogue continues until a stopping condition is met. Once that clicks, the rest of the framework follows naturally.

The core agent types

AutoGen gives you a few building-block agents you compose into a team.

• AssistantAgent — backed by a model, it reasons about the task and proposes answers or code.
• UserProxyAgent — stands in for the human; it can execute code the assistant writes and return the output, and can pause for real human input when configured to.
• Group chat — a manager coordinates several agents talking together, deciding who speaks next, for tasks that need more than two participants.

The conversation that solves the task

A minimal AutoGen setup is two agents. You give the assistant a task. It proposes a solution, often including code. The user-proxy executes that code and returns the result — including any error. The assistant reads the outcome and revises. This loop of propose, run, observe, and correct is exactly how a careful engineer works, and it is what makes AutoGen good at tasks where the first attempt rarely works perfectly.

AutoGen's superpower is the execute-and-correct loop. The agent does not just suggest code — it sees whether the code ran, and fixes it when it did not.

Running code safely

An agent that executes code is genuinely useful and genuinely risky, so this is the part to get right before anything else. Never let agent-generated code run directly on a machine you care about. AutoGen supports executing code inside a container, which isolates it from your real system, and that should be your default. Treat code execution like any untrusted input: sandbox it, limit what it can reach, and never give it credentials or network access it does not need.

• Run code in a container or sandbox, not on your host machine.
• Restrict file-system and network access to the minimum the task requires.
• Keep secrets out of the execution environment entirely.
• Set a maximum number of conversation turns so the dialogue cannot run forever.

When AutoGen is the right choice

Reach for AutoGen when your task benefits from agents critiquing and building on each other, and especially when running code is part of the solution — data cleaning, analysis, generating and testing a script. For workflows that are mostly about calling external services in a known order, a visual tool like n8n or a lighter framework like CrewAI is often simpler. Match the tool to the shape of the problem rather than the other way around.

Prefer it built and managed for you?

AutoGen's code-execution power is exactly why it needs careful operational handling — sandboxing, monitoring, and cost control are not optional in production. BSH Technologies builds and operates production AI agents and automation for businesses, deploying AutoGen-based systems with proper isolation, observability, and guardrails. If you want conversational, code-capable agents running safely for real work, talk to BSH Technologies or see our AI & automation services.