Multi-Agent Systems: When Multiple AIs Work Together
See when splitting work across several agents helps, what it costs, and the common ways teams of agents are organised.
Some jobs are too big or too varied for one agent working alone. Research three competitors, compare them and write a polished one-page report: a single agent has to hold all three investigations in one growing context, and by the third brand it’s running out of room and attention.
A multi-agent system splits the work between several agents, each with its own context, tools and instructions. Common patterns:
- Orchestrator and workers. A lead agent breaks the job into parts, hands each to a worker agent (often in parallel), and assembles the results.
- Writer and reviewer. One agent produces, another critiques, and the first revises: a second opinion that catches mistakes.
- Pipeline. Each agent handles one stage and passes the result on, like an assembly line.
The benefits are real: parallel workers finish sooner, each works in a fresh, focused context, and a reviewer catches errors a lone agent would miss.
So are the costs. Every agent needs its own context, so total token use, and the bill, can be several times higher. Agents can duplicate work if the split is unclear, lose details in hand-offs, or argue in circles without a limit on review rounds. And when something goes wrong, there are more places to look.
The practical advice mirrors the rest of this track: start with one well-built agent. Reach for a team when the work genuinely splits into independent parts, or when a second opinion is worth paying for, and give each agent a clear, separate job.
Key takeaways
- Multi-agent systems split work between agents, each with its own context, tools and instructions.
- Common patterns: orchestrator and workers, writer and reviewer, pipelines.
- Benefits: parallel speed, focused contexts, second opinions.
- Costs: many more tokens, duplicated work, lost details in hand-offs; start with one agent.
Check your understanding
Go deeper (more technical)
Anthropic described a multi-agent research system in 2025 in which a lead agent spawned parallel sub-agents to search; it outperformed a single agent on broad research questions but used many times more tokens than a normal chat. Coding agents use sub-agents in a similar way to explore large codebases without filling the main agent’s context.
Frameworks for multi-agent systems provide hand-off mechanisms, shared state and tracing across agents. Research has also catalogued typical failure modes, such as agents ignoring each other’s output or losing track of the original goal.
The simulation’s times, tokens and scores are illustrative.