Multi-Agent System
A multi-agent system orchestrates multiple specialized AI agents — often LLM-powered, with distinct roles, tools, and prompts — that communicate, coordinate, and divide labor to solve a problem larger or more diverse than any single agent could handle reliably.
Full definition
Common topologies include planner-worker (one agent decomposes, others execute), debater (agents argue to converge), supervisor-subordinate (hierarchical delegation), and swarm (peer-to-peer with shared scratchpad). Frameworks include AutoGen (Microsoft), CrewAI, LangGraph, and Anthropic's reference patterns. Multi-agent improves on single-agent for tasks with parallel sub-goals or where role specialization reduces context bloat.
Why it matters
Multi-agent is also where cost spirals and debugging gets hard: every agent adds latency, tokens, and failure modes. Use multi-agent when sub-tasks genuinely parallelize or specialize; use a single agent with tools otherwise.
Example
A research-report system uses one planner agent, four parallel researcher agents (markets, competitors, financials, regulatory), and one editor agent — producing in 12 minutes what previously took two analysts a day.
Related terms
- Agentic WorkflowAn agentic workflow is a multi-step AI process where a model plans, takes actions through tools, observes results, and iterates toward a goal — replacing single-shot prompting with a loop that reasons, acts, and self-corrects.
- AI Agent / Agentic AIAn AI agent is an LLM-driven system that, given a goal, plans a sequence of steps, calls tools or APIs, observes the results, and iterates until the goal is reached — going beyond single-turn chat to multi-step autonomous action.
- ReAct PatternReAct (Reasoning + Acting) is a prompting pattern that interleaves reasoning steps ("Thought") with tool actions ("Action") and tool results ("Observation"), letting an LLM iteratively decompose a task, query the world, and self-correct.
- Tool Use (LLM)LLM tool use (also called function calling) is the ability of a language model to invoke external functions, APIs, databases, or systems based on the user's request — turning the LLM from a text generator into a controller that can act on the world.
Source & further reading
Primary source: Wu et al. — "AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation" (Microsoft Research) (2023).
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