Run 10+ Claude Code Agents Without Chaos
Run 10+ Claude Code Agents Without Chaos
Running one Claude Code agent is straightforward. Running ten simultaneously on the same codebase is a coordination problem. The difference between productive parallelism and total chaos comes down to configuration discipline.
The CLAUDE.md Foundation
A tight CLAUDE.md file is the single most important factor. When ten agents read the same instructions, consistency depends entirely on how clear and unambiguous those instructions are. Vague guidelines produce ten different interpretations. Specific rules produce aligned behavior.
What belongs in CLAUDE.md for parallel work:
- File ownership boundaries - which directories or modules each agent can modify
- Naming conventions - exact patterns, not suggestions
- Testing requirements - what must pass before a commit
- Branch strategy - how agents name branches and handle merges
Git Worktrees Over Branches
Git worktrees give each agent its own working directory pointing to the same repo. Agents can read each other's work without merge conflicts on uncommitted files. This is strictly better than having ten agents switching branches in the same directory.
The Coordination Patterns
Owner model. Assign each agent a specific area of the codebase. Agent 1 owns the API layer, Agent 2 owns the frontend, Agent 3 owns tests. No overlap, no conflicts.
Queue model. A task queue feeds agents one task at a time. Each task specifies exactly which files to touch. Agents pull, complete, commit, and pull the next task.
Review model. Agents work freely but cannot merge their own changes. A coordinator agent (or human) reviews and merges. This catches conflicts before they hit the main branch.
What Breaks at Scale
Build systems are the first bottleneck. Ten agents running builds simultaneously will overwhelm most machines. Stagger builds or use remote build infrastructure.
Test flakiness becomes intolerable at scale. A test that fails 2% of the time will fail constantly when ten agents run it in parallel. Fix flaky tests before scaling agents.
Context staleness is the subtlest problem. An agent that started work 20 minutes ago might be operating on assumptions that three other agents have already invalidated.
The Rule
If your agents need to communicate with each other in real time, you have too many agents or your task decomposition is wrong. Good parallel work is embarrassingly parallel - each agent's task is independent by design.
Fazm is an open source macOS AI agent. Open source on GitHub.