Running AI coding agents in parallel with git worktrees
Three agents on one checkout means trampled files and lost work. Giving each task its own git worktree, its own model and a deterministic merge step fixed that for me.
6 posts across articles, notes and newsletter issues.
Three agents on one checkout means trampled files and lost work. Giving each task its own git worktree, its own model and a deterministic merge step fixed that for me.
Clone, install, run the tests, then separate real regressions from environmental noise. Why I built repo-doctor as a deterministic pipeline first and an agent second.
A short note from building cron-for-agents. The first version kept its watermark in memory, a restart erased it, and an agent reprocessed a week of old data. State belongs on disk.
Plain cron runs your agent on time, then wastes tokens reprocessing old data and spams you with identical summaries. Watermarks and content-hash dedup fix both.
Two months of unglamorous infrastructure. A cron tool that does not spam duplicate summaries, sandbox M-Pesa tools for agents, and the start of a proper way to test how agents fail.
Worktrees do not disappear when an agent's task ends. A short note on the cleanup step I almost skipped while building worktree-orchestrator, and the disk space and confusion it saves.