A coding task is handled by a small team of specialized agents: a planner agent that breaks the request into subtasks, a coder agent that writes code for each subtask, and a reviewer agent that critiques the code and asks for revisions, all exchanging messages with each other in a structured conversation until the reviewer is satisfied. What is the defining feature of this multi-agent design, compared to handling the whole task with a single agent working alone?
- Only one agent in the group is ever allowed to call an external tool, and the rest may only produce plain text
- The overall task is decomposed across multiple specialized agents that communicate with each other through a structured conversation, so different roles (planning, generating, critiquing) are handled by different agents rather than one agent doing everything internally
- Every agent in the group must use the exact same system prompt, since giving them different instructions would prevent them from communicating
- The design requires no termination condition at all, since a reviewer agent will keep the conversation open indefinitely by design
Why B? And why not the others?
Correct answer: B. The overall task is decomposed across multiple specialized agents that communicate with each other through a structured conversation, so different roles (planning, generating, critiquing) are handled by different agents rather than one agent doing everything internally
The defining feature of a multi-agent design like this is splitting a task across separate, specialized conversable agents -- one for planning, one for generating a solution, one for reviewing it -- that pass messages to each other rather than a single agent handling every role internally in one uninterrupted stream of reasoning; frameworks such as AutoGen build agents specifically to converse with each other in this way, mixing roles and even combinations of models, human input, and tools per agent. The option restricting tool use to only one agent in the group is wrong -- nothing about a multi-agent design requires that only one participant may call tools, and different agents can each have their own tools. The option requiring every agent to share the exact same system prompt is wrong and actually contradicts the point of specialization -- giving each agent a distinct role-specific prompt is what lets the planner, coder, and reviewer behave differently from one another. The option claiming no termination condition is needed is wrong; a multi-agent conversation still needs an explicit stopping point (such as the reviewer signalling approval or a reply-count limit), or it risks looping the same way a single-agent loop can.
Source: Wu et al., 'AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework' (2023), arXiv:2308.08155