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What Is a Multi-Agent System?
Several specialized AI agents collaborate to solve complex problems.
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AiTechWorlds
AiTechWorlds
Multi-agent systems use several specialized AI agents that collaborate to solve complex problems. This visual guide explains agent roles, communication, orchestration patterns, and when a team of agents outperforms a single agent.
Several specialized AI agents collaborate to solve complex problems.
Specialized agents handle subtasks better than one generalist.
Like a team — planner, researcher, coder, reviewer.
A controller coordinates which agent acts when.
Agents pass messages and results to each other.
A common workspace keeps agents aligned.
Agents work one after another in a pipeline.
Agents work simultaneously on independent parts.
A manager agent delegates to worker agents.
Agents review each other to improve quality.
Rules decide outcomes when agents disagree.
Agents share access to tools and data.
More agents mean more tokens and complexity.
Errors can cascade across agents.
CrewAI, AutoGen, and LangGraph build multi-agent apps.
Research, software building, and complex analysis.
Use multi-agent only when complexity justifies it.
Measure end-to-end success, not just single steps.
Clear roles, good handoffs, and strong guardrails.
Agent teams may automate entire workflows.
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