Git Worktrees for AI Development

You are running Claude Code on a feature branch. The agent has been working for twenty minutes, it has read your codebase, built up context, and started making real progress on the authentication rewrite. Then a Slack message appears: production is down, someone needs a hotfix on main, and they need it now. In the old workflow, you stash your changes, switch branches, lose everything your AI agent built up, fix the bug, push, switch back, and spend ten minutes getting the agent re-oriented to what it was doing. If you were running two agents simultaneously on the same directory, the situation is worse — both agents touching package.json, both generating edits to the same files, and the second writes silently, overwriting the first. No warning. No error. Just corrupted work you discover an hour later when tests fail in a way that makes no sense. Git worktrees eliminate this entire class of problems. They are not a new invention — the feature has been in Git since version 2.5, released in 2015 — but the AI coding wave of 2025–2026 made them essential infrastructure. One .git directory,…