Multi-agent system

A multi-agent system divides a hard problem among several focused agents instead of asking one generalist to do everything. Each member is an AI agent with its own role, tools, and instructions — a researcher, a planner, a coder, a critic — and the system gets work done by letting these specialists cooperate: one delegates a subtask, another completes it, a third reviews the result and sends it back if it falls short. The whole is more capable, and more reliable, than any single agent stretched across every job.

How it works: a goal arrives and is decomposed into pieces that match each agent's strength. The agents pass structured messages to share intermediate results, ask for help, or challenge a conclusion. Because every agent keeps a small, sharp context, it stays accurate where a single bloated prompt would drift. This coordination is rarely free-form — it is governed by orchestration that decides who runs, in what order, and what happens when a step fails. The pattern is a natural extension of agentic AI: the same goal-seeking loop, now distributed across collaborators.

Why it matters and a concrete example: imagine shipping a market report. A retrieval agent gathers sources, an analyst agent extracts figures and trends, a writer agent drafts the narrative, and an editor agent fact-checks the draft against the sources and flags anything unsupported before it reaches you. Each role can be tuned, guard-railed, and improved independently — and the editor's review pass catches mistakes the writer alone would miss. That separation of concerns is exactly why teams reach for multi-agent designs on complex, multi-stage work.