trendingAI & ML — INTRODUCTION
Multi-Agent Systems: How AI Agents Work Together
A single AI agent can already reason, call tools, and chain steps together. But some tasks are broad enough that one agent handling every sub-step itself starts to strain — its context window fills up, and steps that could run at the same time end up running one after another instead. A multi-agent system is the answer: splitting the work across several agents instead of one.
The Quick Answer
A multi-agent system splits one task into smaller sub-tasks and hands each one to its own agent, rather than having a single agent do every step itself. The most common shape is orchestrator-worker: a lead agent breaks the task apart, spawns sub-agents to handle each piece independently and often in parallel, then combines their results into one final answer. Each sub-agent works with its own separate context, so it never gets bogged down by details that belong to another sub-agent’s slice of the problem.
Why Split One Agent Into Many?
Two limits push teams toward multiple agents instead of one bigger one. Context window pressure is the first: a single agent researching several independent things keeps adding every result to the same growing context, and a detail relevant to one sub-task can crowd out or confuse the model’s handling of another. Wasted parallelism is the second: if three sub-tasks don’t depend on each other at all, a single agent still has to do them one after another, in sequence, because it’s only one agent working at a time. Splitting the work across separate agents fixes both — each agent gets a small, focused context, and independent sub-tasks can genuinely run at the same time.
See the Difference: One Agent vs. Several
Toggle between the two approaches on the same research task to see exactly what changes:
What the Experts Say
Anthropic’s own production research system is a real, measured example of this pattern: a lead agent (Claude Opus 4) decomposes a research task and spawns sub-agents (Claude Sonnet 4), each with its own isolated context, working independently before reporting back a condensed summary. Anthropic found this multi-agent setup outperformed a single-agent version by 90.2% on their internal research evaluation — though it used roughly 15× more tokens to get there, since running several agents genuinely costs more than running one.
Source: Anthropic, “How We Built Our Multi-Agent Research System”
This exact case study is covered in more architectural depth — including why sub-agents are deliberately kept unaware of each other — in Context Engineering Advanced. This topic picks up from here and goes deeper into how the coordination itself works.
Fun Fact
More agents is not automatically better. Anthropic’s own analysis found that token usage alone explained about 80% of the performance variance on their benchmark — meaning most of the gain came from giving sub-agents room to actually explore, not from some clever coordination trick. A multi-agent system that spawns agents for a task that doesn’t actually split into independent pieces just spends more tokens for no real benefit.
Test Yourself
What is the core idea behind a multi-agent system?
In the orchestrator-worker pattern, what does the orchestrator typically see from each sub-agent?
According to Anthropic’s own published research, what was the main trade-off of their multi-agent research system compared to a single agent?
Ready to see exactly how the orchestrator hands work to sub-agents, and how their results get combined? Continue to the Intermediate level →