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Claude Code supports multi-agent orchestration — spinning up multiple AI agents working in parallel to complete complex tasks faster than a single agent could alone.

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How it works

When you ask Claude to handle a large or parallelizable task, it can:
  1. Break the task into independent subtasks
  2. Spawn sub-agents via AgentTool for each subtask
  3. Each sub-agent runs independently with its own tools and context
  4. Results are collected and synthesized by the parent agent

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Spawning sub-agents

You don’t need to do anything special — just ask: 
> Refactor all the API handler files to use async/await consistently
> Write unit tests for every function in src/utils/
> Review each module in src/services/ for security issues
Claude decides when parallelization helps and spawns agents accordingly.

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Team-based workflows

For larger coordinated work, Claude can create a named team of agents: 
> Create a team to migrate the entire codebase from JavaScript to TypeScript
TeamCreateTool provisions the team. Each agent in the team can communicate via SendMessageTool, enabling coordination patterns like:
  • One agent reads files, others write changes
  • A coordinator agent breaks down work and delegates
  • A reviewer agent validates results from worker agents
Teams are cleaned up automatically or with TeamDeleteTool.

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Coordinator mode

The coordinator/coordinatorMode.ts subsystem handles multi-agent orchestration at a higher level — managing agent lifecycles, work distribution, and result aggregation for complex swarm tasks.

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Plan before execution

For large multi-agent tasks, enter plan mode first: 
> Enter plan mode: describe how you'd parallelise migrating all our API tests to the new test framework
Review the plan, then approve execution: 
> Looks good, execute the plan

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Example: parallel test generation

> Generate unit tests for every file in src/services/ — use multiple agents to do this in parallel
Claude will:
  1. List all files in src/services/ using GlobTool
  2. Spawn one sub-agent per file (or group small files)
  3. Each agent reads its assigned file and writes a test file
  4. Parent agent reports completion

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Limitations

  • Sub-agents share your permission settings but each gets a fresh context window
  • Very large swarms may hit API rate limits — Claude handles backoff automatically
  • Sub-agent work is visible in the parent session’s output
Multi-agent workflows shine for tasks with many independent files (e.g., adding types to every module, generating tests, or updating imports across a large codebase).

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