What is AutoGen and how does it coordinate multiple agents?

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AutoGen is Microsoft Research's multi-agent conversation framework. The central abstraction is structured chat between agents: UserProxyAgent (often plays the human or executes code), AssistantAgent (LLM-backed worker), and GroupChatManager (orchestrates turn-taking across a list of agents in a shared conversation). Tasks get solved by agents exchanging messages until a termination condition. AutoGen v0.4 introduced a layered architecture (Core / AgentChat / Extensions) for distributed and cross-language agents. Best fit: exploratory, conversation-shaped coordination. Trade-off vs LangGraph: less explicit about routing. The chat manager decides who speaks next from a policy, not a graph.

Your answer

Quick reference

TL;DR

AutoGen is Microsoft Research's multi-agent chat framework with three core actors, UserProxyAgent, AssistantAgent, GroupChatManager, and a turn-selection policy instead of a graph.

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Easy to grasp

Picture a small meeting where one person takes notes, several specialists answer questions, and a moderator decides who speaks next. The note-taker also has hands and can run errands the group asks for. The specialists only talk. The moderator listens to the conversation and picks the next speaker based on what just got said. The meeting keeps going until somebody says we are done. AutoGen sets up exactly this kind of meeting, except the participants are language-model bots and the errands are things like running a snippet of code or fetching a file.

Key concepts

LLM Frameworks Agents

Detailed answer & concept explanation

~6 min read

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.

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Quick scan · 2–4 min

Everything important, quickly.

4 minutes: the three agent types, the GroupChat manager, the v0.4 layered architecture, and the LangGraph contrast.

Real-world examples

Real products, models, and research that use this idea.

Microsoft Research's AutoGen 0.4 documentation describes the Core / AgentChat / Extensions layering used in production deployments.
Microsoft's Magentic-One reference agent is built on AutoGen and uses GroupChat for multi-modal task orchestration.
Several enterprise pilots reported at NeurIPS 2025 use AutoGen for code execution heavy data-engineering pipelines.
The OpenAI Swarm / Agents SDK is the minimalist counterpoint: same multi-agent territory, far thinner abstraction.

Interviewer follow-ups

What an interviewer would ask next. Try answering before peeking at the approach.

QHow does AutoGen v0.4's actor-model Core enable cross-process agent deployment?

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QCompare GroupChatManager's speaker-selection policy to LangGraph's conditional edges.

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QWhen would you prefer AutoGen over CrewAI for a multi-agent task?

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Don't say this

Red flags and common mistakes that signal junior thinking. Click to expand.

Most common mistake

Treating AutoGen as a graph framework like LangGraph and being surprised when the next speaker is chosen by a policy at runtime instead of an explicit edge.

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60-second cheat sheet

The night-before-the-interview bullets. Scan these on the way to the call.

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References

Primary sources. Skim if you want the original framing.