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The simplest parallel pattern. Spawn N agents, each handling an independent slice, then synthesize results. How it works:
  1. Break work into independent units (by file, module, or concern)
  2. Spawn one agent per unit with run_in_background: true
  3. Each agent works autonomously and returns results
  4. The parent synthesizes when all complete
Best for: research across multiple areas, independent file analysis, running tests in parallel. Key constraint: tasks must have no shared state and clear file boundaries. If agents edit overlapping files without worktree isolation, they’ll overwrite each other.
Context budget: Keep the orchestrator at ~10-15% of its context window. Have subagents write outputs directly to disk and return only a brief confirmation — this gives each subagent a fresh, full context window with no accumulated noise.
With Agent Teams, the shared task list adds flexibility — teammates can self-claim unassigned tasks rather than having all work pre-assigned.

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Examples in the wild