Multi-agent System
Also: agent swarm
A setup where multiple AI agents work together on a complex task — each handling a specific part, coordinated by an overall system. For example: one agent researches the topic, another drafts the report, another checks it for errors. Using multiple specialized agents often produces better results than asking one agent to do everything. It's how companies automate complex workflows that have too many moving parts for a single AI to handle well.
In practice
You're building a research pipeline. One agent searches the web, another reads and summarizes each result, a third synthesizes them into a report, and a fourth checks it for errors. Each agent does one thing well. A multi-agent system coordinates all four — the output of each feeds the next — to complete work no single agent handles reliably alone.
Related concepts
Where Multi-agent System shows up
2 articlesSubagents, orchestrators, parallelism, and state management. The patterns that work and the ones that look good until they hit production.
Most multi-agent systems are shipped without ever measuring if they beat a single well-prompted Claude call. Here is the evaluation methodology: golden datasets, LLM-as-judge scoring, ablation testing, and the cost/latency tradeoff equation.