AutoGen (and its community fork AG2) is a framework where multiple agents solve a task by exchanging messages, and its power is in the conversation…
Recall that in AutoGen agents solve tasks by talking.
AutoGen, and its actively developed fork AG2, model a solution as a conversation between agents. Each agent has a role and can reply, call tools, or execute code; the exchange of messages between them is the program. One agent proposes, another critiques or runs code, and the dialogue drives toward the goal.
Because the conversation is the logic, the real design work is choosing the conversation pattern: how many agents, who talks to whom, and in what order. The patterns below are the vocabulary for that.
Learn the two most common conversation shapes.
The simplest pattern is two agents in dialogue — classically an assistant that reasons and a proxy that executes tools or code and represents the user. They alternate: the assistant proposes a step, the proxy runs it and returns the result, until the task is done. Many tasks need nothing more than this pair.
For several specialists, group chat adds a manager that decides who speaks next. On each turn the manager selects the most relevant agent — by round-robin, by a rule, or by asking a model to choose — and that agent contributes. This suits tasks needing distinct experts (a planner, a coder, a reviewer) collaborating in one room.
The manager's speaker-selection strategy is the key knob: distinct agent roles help it (and the agents) route correctly.
Compose conversations in series and inside one another.
Sequential chats run a series of separate conversations where each one's result carries into the next — useful for a pipeline of distinct stages (gather requirements, then design, then implement), with a clean handoff between phases rather than one long thread.
Nested chats let an agent, when it receives a message, spin up its own inner conversation to work out a sub-problem before answering the outer one. From the outside it looks like a single agent, but internally a whole team may be deliberating. This encapsulates complexity — the outer flow stays simple while a hard step delegates inward.
Pick the right pattern and avoid the classic failures.
Choose by the task's shape. A single reasoning-plus-execution task: two-agent chat. Several experts collaborating openly: group chat. A fixed multi-stage pipeline: sequential chats. A step that hides its own complexity: nest a chat inside an agent. Start with the simplest pattern that fits and only add structure when the task demands it.
Whatever the pattern, define clear agent roles and a firm termination condition.
Watch for: no reliable termination, so agreeable agents chat forever and burn tokens (always cap turns and add a stop signal); vague or overlapping roles that confuse speaker selection; over-using group chat when two agents would do; running model-generated code outside a sandbox; and ignoring cost — every message is an LLM call, so patterns with many agents multiply spend fast.
AutoGen/AG2 solves tasks through agent conversations, so patterns are the design surface. Two-agent chat pairs a reasoner with an executor; group chat lets a manager pick the next of several specialists; sequential chats chain distinct stages; nested chats hide a sub-team inside one agent. Match the pattern to the task's structure, start simple, give agents clear roles, sandbox code execution, and always set a firm termination condition and watch per-message cost.
Design an AutoGen/AG2 system that researches a topic, writes a report, and fact-checks it. Choose which parts use group chat versus sequential chats, whether any step should be a nested chat, and write the termination condition that stops the whole run.
How does AutoGen/AG2 model a solution?
In AutoGen the conversation is the program, so the main design choice is the conversation pattern among agents.
What defines the group-chat pattern?
Group chat coordinates multiple experts via a manager's speaker-selection strategy, which is why distinct roles matter.
What is a nested chat?
Nested chats encapsulate complexity: the outer flow sees one agent while a team deliberates internally on a hard step.
What is the most important safeguard across all these patterns?
Without reliable termination, agreeable agents loop and burn tokens; clear roles and sandboxed execution matter too.