A multi-agent system isn’t automatically smarter. Five confused agents can make a bigger mess than one clear agent. It only works when the roles, handoffs, context, and review gates are designed well, so the point isn’t more agents. It’s matching the right worker to the right part of the job.
Think of a general contractor. They don’t personally hang every sheet of drywall, install every pipe, and wire every light. They coordinate the job, assign specialists, and make sure the house comes together. A multi-agent system works the same way: one orchestrator owns the plan, one subagent researches, another edits, another tests, another reviews.
How it shows up
In Claude Code or Codex work, a main agent might send one subagent to inspect the codebase, another to write tests, and another to review the final diff, while it stays in conversation with you. We also use cross-model review: Claude builds, Codex reviews, Claude fixes, and the loop continues until the work passes a higher bar. Sometimes these systems reach tools through MCP or APIs. All of it depends on handoffs: the research agent has to return findings in a useful format, and the orchestrator needs enough visibility to catch conflicts.
Why you care
Multi-agent systems help when the work has natural parts: research, drafting, testing, and review are different kinds of work, and forcing one agent to carry all of it makes it switch roles constantly. But you don’t start here. If the work isn’t clear enough for one agent, adding more usually hides the confusion. Start with the workflow, define the inputs and outputs, identify the handoffs, then decide whether multiple agents help.