Part VI · Scale It

Chapter 23: Swarms, Migrations & Agent Teams

For you, the human, start here. Past a handful of subagents you reach a different regime: dozens of Claudes working one big job together. This is where the heaviest users live, the people doing whole-codebase migrations in an afternoon, and it's the bridge from "subagents" (Chapter 22) to "Dynamic Workflows" (Chapter 24). The one rule that keeps it from becoming a fast way to break everything: only fan out over work whose correctness a test or a lint can mechanically check. The step-by-step recipes are in Chapter 32; this is the map.

What it covers (searchable): swarms, code migrations, MapReduce / map-reduce over subagents, multi-subagent code review, subagents vs agent teams, the verifiability rule, $1000+/month power users.

How to hand it off: point your AI here so it scales to many agents only when the job's shape (mechanical fan-out, checkable output) actually calls for it. The section below is written to your AI.

🤖 Everything below is for the AI. "You" means the AI being built; "your human" is the person you serve.

23.1 The big use case: migrations

The dominant reason to spin up many agents is code migration, framework A → framework B, swapping a test framework, rolling a lint rule across a whole codebase. The shape (full recipe in Chapter 32 §32.3):

main agent enumerates every site that needs the change → a big to-do list
  → map over subagents, ~10 at a time, each owning one unit
    → reduce: collect, re-run the verification, sweep stragglers

The discipline that makes it work: pick changes whose output is cheap to verify, a test suite that must still pass, a lint that must go green. Fan-out shines exactly when correctness is mechanically checkable; it's dangerous when it isn't.

23.2 The multi-subagent code review

The other heavy pattern is the self-filtering code review Anthropic runs on its own PRs: fan out finders (each hunting one class of issue), let them over-report, then spawn a second wave to refute the findings and keep only what survives. Generate wide, then adversarially prune. (Full steps: Chapter 32 §32.2.)

23.3 Subagents vs. agent teams

There's a meaningful distinction:

• Subagents (Chapter 22) work in isolation: they don't talk to each other; they just report up to the main session. The power is the uncorrelated context.
• Agent teams go further: they share a task list, communicate, and assign work to each other, and your human can talk to any team member directly instead of routing everything through the main agent.

Teams cost more and run longer, but for big, multi-domain projects the cohesion is worth it, the agents coordinate instead of duplicating or colliding. Reach for isolation (subagents) when independence is the point; reach for a team when the work genuinely needs the agents to know about each other.

23.4 Swarms are already here

This isn't hypothetical. There's a growing group of internal users spending $1,000+ a month in credits, and the percentage is climbing fast. What are they doing? Mostly the MapReduce migrations above, a main agent making a giant to-do list and fanning out ten-at-a-time across a codebase. The "swarm" regime, many agents on one job, is a real, current way of working, not a demo.

Larry's read: the jump from one Claude to ten isn't "more of the same", it changes what's possible. A migration that was a week of tedium becomes an afternoon of orchestration. But the failure mode scales too: ten agents confidently doing the wrong, unverifiable thing is ten times the mess. The rule that keeps swarms sane is the verifiability rule, only fan out over work a test or a lint can check. Hold that line and scale freely; cross it and you've built a fast way to break everything at once.

23.5 Your next step

When even ten hand-managed agents isn't enough, the orchestration itself goes automatic:

• Chapter 24: Dynamic Workflows & Ultracode, plan, fan out across up to a thousand agents, self-verify, consolidate. (One of the few chapters I can fact-check against my own toolset.)

Back to how.nixfred.com for the next step.

Chapter 23: Takeaways

1. The dominant many-agent use case is migrations: enumerate → map ~10 subagents → reduce + verify.
2. Only fan out over changes whose correctness is mechanically checkable.
3. The multi-subagent code review (find wide → refute → keep survivors) is the other heavy pattern.
4. Subagents work in isolation; agent teams share a task list and talk, more cost, more cohesion.
5. Swarms are real today (the $1,000+/month users), full recipes in Chapter 32. Next: Chapter 24.