Agentic AI & agents
Multi-Agent Systems Explained
More agents are not automatically better. Here is when splitting up the work actually helps.
A multi-agent system splits a task across several specialized agents that each focus on a narrower job — one researches, one writes, one reviews — instead of asking a single generalist agent to do everything itself.
Multi-agent systems can improve quality and reliability on genuinely complex tasks, but they also add real coordination overhead and cost. They are a deliberate architectural choice, not a default upgrade over a single agent.
Key takeaways
Why split a task across agents at all
A single agent handling research, writing, and fact-checking simultaneously can lose focus on any one of those jobs, since its context window and reasoning are stretched across all of them at once. Splitting these into specialized agents — a researcher, a writer, a reviewer — lets each one work with a narrower, clearer objective.
This specialization can noticeably improve output quality on genuinely complex tasks, similar to how a team of specialists can outperform one generalist on a large project.
Common multi-agent patterns
A common pattern is a coordinator agent that breaks down the overall goal and delegates sub-tasks to specialist agents, then assembles their results. Another is a pipeline, where agents work in sequence, each handing its output to the next.
Less common but powerful is a reviewer pattern, where one agent’s output is checked or critiqued by a second agent before being finalized, catching errors a single agent working alone might miss.
The real cost of adding more agents
Every additional agent adds coordination overhead: more model calls, more places for errors to occur, and a harder debugging problem when something goes wrong across multiple agents interacting. Costs scale roughly with the number of agents and hand-offs involved.
Before adopting a multi-agent architecture, confirm that a single, well-designed agent with good tools and planning genuinely cannot handle the task — many problems that look like they need multiple agents are solved just as well by one agent with a clearer prompt and better tools.
Put this into practice