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AutoGen: Conversational Multi-Agent Systems

Microsoft's conversational multi-agent framework where agents exchange messages to collaborate, critique, and complete tasks — the v0.4 async rewrite, GroupChat patterns, and when to reach for it over LangGraph.

By Amit UpadhyayPublished 12 MayUpdated 23 Aug 202612 minArticle 7 of 12 in Agentic Systems

In AutoGen v0.4 a user proxy starts a GroupChat where a selector picks the next speaker among planner, coder and critic agents, running asynchronously until a termination condition stops the chat.

On this page

The idea at a glance

The problem

When

A task benefits from several agents talking to each other, such as a coder and a reviewer

The obvious move

Starting a group chat and letting the agents talk until they agree

Fails because

Agents are polite and persistent: without a clear stop condition they keep refining, praising and re-reviewing until a turn limit or the budget runs out

The fix

AutoGen works by

Few agents with distinct roles, an explicit termination condition, per-conversation token budgets, and a migration path to Microsoft Agent Framework

At the cost of

More model calls than one agent, conversations that are harder to debug, and a framework in transition

The scenario every mode follows

“Write a function that parses ISO 8601 durations, with tests.”

A coder agent and a reviewer agent that write small utility functions with tests
~200 tasks a week from a developer tool
gpt-6-1-sol

Where the obvious approach breaks

40+ turns of polite back-and-forth on a 20-line function

If you run the chat with no termination condition other than a high turn limit.

Without a stop rule, the reviewer kept suggesting small improvements and the coder kept accepting them: renamed variables, extra comments, a docstring rewrite. The function was correct after turn 4; the conversation ran to the 40-turn limit and cost ten times what the task needed.

Helps to know firstMulti-agent architecture

When One Prompt Has to Play Every Role

Single-agent LLM systems hit a ceiling fast: one prompt has to be planner, executor, critic, and editor at the same time. The result is a mega-prompt that nobody can debug and a context window that fills up in three turns. AutoGen — Microsoft Research's conversational multi-agent framework — answers this by letting you spin up several specialised agents and have them talk to each other (and optionally to a human) until the work is done.

If your single prompt is doing too much

Your single "do everything" prompt has grown past 4 KB and is still missing edge cases
You want a coder agent, a reviewer agent, and a tester agent, but don't want to hand-roll the message bus
You need a human-in-the-loop checkpoint without rewriting the agent graph

Pro deep dive12 min full read

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This is one of our advanced production deep dives. Pro unlocks the rest of it, every other deep dive, and the See, Animate, Test and Build modes on every topic.

What's inside

02
The Mental Model
03
Minimal Working Example (AutoGen v0.4, async)
04
What v0.4 Fixed
05
AutoGen in Late 2026: The Agent Framework Path
06
Common Failure Modes
07
When to Reach for AutoGen vs LangGraph
08
Production Checklist
09
Key Takeaways

See plans

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Next: See →

AutoGen multi-agent GroupChat conversational-agents microsoft

← PreviousFrom Chatbot to Agent: Adding Tools, Memory, and Planning to a Simple Chat Interface Next →Episodic Memory: Agents That Learn From Experience

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All Agentic Systems articles →

Agentic Systems11 min

Building a Production Tool Registry

Design and maintain a central catalogue of agent tools — versioning, capability tagging, and dynamic discovery for scalable agent systems.

Agentic Systems14 min

Managing Short-Term Memory in Agents

Manage the context window as working memory — what to include, summarise, or drop to keep agents coherent across long conversations.

Agentic Systems12 min

Designing Reliable Planning Loops

Build reliable thought-action-observation cycles that let agents decompose complex goals into executable steps without going off-track.

Discussion

Agentic SystemsProAdvanced

AutoGen: Conversational Multi-Agent Systems

By Amit UpadhyayPublished 12 MayUpdated 23 Aug 202612 minArticle 7 of 12 in Agentic Systems

On this page

The idea at a glance

The problem

When

A task benefits from several agents talking to each other, such as a coder and a reviewer

The obvious move

Starting a group chat and letting the agents talk until they agree

Fails because

Agents are polite and persistent: without a clear stop condition they keep refining, praising and re-reviewing until a turn limit or the budget runs out

The fix

AutoGen works by

Few agents with distinct roles, an explicit termination condition, per-conversation token budgets, and a migration path to Microsoft Agent Framework

At the cost of

More model calls than one agent, conversations that are harder to debug, and a framework in transition

The scenario every mode follows

“Write a function that parses ISO 8601 durations, with tests.”

A coder agent and a reviewer agent that write small utility functions with tests
~200 tasks a week from a developer tool
gpt-6-1-sol

Where the obvious approach breaks

40+ turns of polite back-and-forth on a 20-line function

If you run the chat with no termination condition other than a high turn limit.

Helps to know firstMulti-agent architecture

When One Prompt Has to Play Every Role

If your single prompt is doing too much

Your single "do everything" prompt has grown past 4 KB and is still missing edge cases
You want a coder agent, a reviewer agent, and a tester agent, but don't want to hand-roll the message bus
You need a human-in-the-loop checkpoint without rewriting the agent graph

Pro deep dive12 min full read

Keep reading with Pro

This is one of our advanced production deep dives. Pro unlocks the rest of it, every other deep dive, and the See, Animate, Test and Build modes on every topic.

What's inside

02
The Mental Model
03
Minimal Working Example (AutoGen v0.4, async)
04
What v0.4 Fixed
05
AutoGen in Late 2026: The Agent Framework Path
06
Common Failure Modes
07
When to Reach for AutoGen vs LangGraph
08
Production Checklist
09
Key Takeaways

See plans

Already Pro? Sign in from the top bar and this page unlocks.

Next: See →

AutoGen multi-agent GroupChat conversational-agents microsoft

← PreviousFrom Chatbot to Agent: Adding Tools, Memory, and Planning to a Simple Chat Interface Next →Episodic Memory: Agents That Learn From Experience

Continue reading

All Agentic Systems articles →

Agentic Systems11 min

When One Prompt Has to Play Every Role

Keep reading with Pro

The Mental Model

Minimal Working Example (AutoGen v0.4, async)

What v0.4 Fixed

AutoGen in Late 2026: The Agent Framework Path

Common Failure Modes

When to Reach for AutoGen vs LangGraph

Production Checklist

Key Takeaways

Continue reading

Building a Production Tool Registry

Managing Short-Term Memory in Agents

Designing Reliable Planning Loops

Discussion

When One Prompt Has to Play Every Role

Keep reading with Pro

The Mental Model

Minimal Working Example (AutoGen v0.4, async)

What v0.4 Fixed

AutoGen in Late 2026: The Agent Framework Path

Common Failure Modes

When to Reach for AutoGen vs LangGraph

Production Checklist

Key Takeaways

Continue reading

Building a Production Tool Registry

Managing Short-Term Memory in Agents

Designing Reliable Planning Loops

Discussion