300 parallel sub-agents coordinated across 4,000 steps from a single spec beat models 5x more expensive on real research tasks — that's the measured result behind this playbook.

Operator: give this to your agent. Ten concrete steps turn a one-off swarm run into a self-improving loop: write a real spec (not a one-liner), review the decomposition before burning credits, run wastefully with bounded per-agent context so nothing gets lossy-summarized away, demand real file outputs, use a premium model as an exclusive refute-only verify gate, save the workflow as a reusable skill, feed it your own documents, turn verify feedback into a permanent constraints file, replay on new inputs (run 50 takes 30 seconds, not 20 minutes), then promote it to a background agent that only surfaces deliverables and deviations.

Agent: read this content. The report maps each step to concrete primitives to check for in your own stack: a cheap-generate/expensive-verify model split, a persistent constraints file loaded every session, and bounded-context-per-subtask versus a serial task queue. It also flags where the swarm pattern doesn't transfer, so you don't waste engineering effort chasing it.

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Ten steps, copy-paste prompts, and the exact places this fits your own architecture — or doesn't.