## Definition **Parallel agents via worktrees** is the practice of running independent AI coding sessions in parallel using `git worktree` to give each agent its own working directory while sharing the same `.git` data. Recommended by Anthropic's Claude Code documentation and adopted by Cursor's "Parallel Agents" feature. ## Setup ```bash git worktree add ../app-experiment-a -b experiment/a git worktree add ../app-experiment-b -b experiment/b git worktree add ../app-experiment-c -b experiment/c ( cd ../app-experiment-a && claude "implement approach A from plan.md" ) & ( cd ../app-experiment-b && claude "implement approach B from plan.md" ) & ( cd ../app-experiment-c && claude "implement approach C from plan.md" ) & wait ``` ## Three Benefits 1. **Isolation.** Each agent has its own working tree; no file conflicts. 2. **A/B comparison.** Compare implementations against the same spec. 3. **Time leverage.** Three 20-minute runs in 20 minutes total, not 60. ## When to Use - True architectural alternatives where comparison is the point. - A spec where you genuinely don't know which approach is best. - High-stakes changes where redundancy is cheap insurance. ## When NOT to Use - Sequential dependencies (frontend needs the backend shape first). - Tasks where the right answer is obvious — you're burning tokens. - Anything touching shared external state (databases, queues) — isolation is filesystem-level, not database-level. ## Practical Limits - **Disk usage.** A 20-minute session on a ~2GB codebase has been observed to use ~10GB of worktree storage. - **Cognitive overhead.** Most teams find 2–4 parallel agents the practical ceiling; beyond that, coordinating outputs outweighs the parallelism. - **Merge conflicts with yourself.** Worktrees can create conflicts you don't notice until merge time. ## Related - [[Specialized Agent]] - [[Sequential Pipeline]] - [[Builder-Critic Pattern]]