What is Multi-Agent System?
An AI architecture where multiple specialized AI agents collaborate, each handling a sub-task, to complete complex goals no single agent could handle alone.
Definition
A multi-agent system (MAS) is an AI architecture composed of multiple individual AI agents that communicate, coordinate, and collaborate to achieve goals that would be too complex or broad for a single agent. Each agent specializes in a domain (research, writing, coding, verification) and agents pass work between each other. Orchestrator agents plan and assign; worker agents execute; reviewer agents check quality.
Why it matters
Single agents have limitations: context window constraints, single-model reasoning bottlenecks, and inability to parallelize work. Multi-agent systems solve these. A complex task like "do a full competitive analysis and produce a 20-page report" is handled by specializing: one agent searches, one synthesizes, one writes, one edits. The result is higher quality and faster than a single agent trying to do everything.
How it works
Common multi-agent patterns: (1) Orchestrator-worker — an orchestrator breaks a goal into tasks and routes each to a specialized worker agent. (2) Pipeline — agents are arranged as a sequence, each transforming the output of the previous. (3) Debate/review — agents independently produce outputs, then a judge agent evaluates and selects the best. (4) Parallel execution — multiple agents work on different sub-problems simultaneously, then merge results.
Examples in practice
Multi-agent research pipeline
An orchestrator receives "research competitors in the AI education space." It spins up 5 parallel research agents (one per competitor), each searching and summarizing. A synthesis agent merges the outputs into a structured report.
Multi-agent software development
One agent writes code, a second reviews it for bugs, a third writes tests, a fourth runs the tests and reports failures back to the first agent to fix. Each agent specializes in one phase of the software development lifecycle.