Nine practical lessons on Git worktree coordination for AI development, with pinned sources, seven teaching-model cases and explicit runtime limits.
Latest article
Trace control from an operation through configured commands, approval and pre/post hooks, including startup dependencies and merge behavior.
By publication date
01 → 09
Trace control from an operation through configured commands, approval and pre/post hooks, including startup dependencies and merge behavior.
Compare manual worktree management, Worktrunk lifecycle automation and agent orchestration without treating them as interchangeable products.
Separate package installation from shell integration and repository hooks, with a versioned rollout and a narrow rollback plan.
Learn the smallest worktree exercise, separate successful creation from shell navigation, and avoid accidental hooks or cleanup.
Design a small audit report for branches, configuration and unresolved risks without automatically approving hooks, merging code or deleting worktrees.
Understand how Worktrunk coordinates branches, directories and lifecycle commands, and why a separate worktree is not an agent sandbox.
Build a practical ledger for checkout preparation, builds, model calls and review time before deciding whether more agents improve throughput.
Review actual command authority, optional model data flow and recovery limits before automating merge or cleanup.
Walk through approve_command_batch and approval storage to understand why accepting a command is different from remembering that acceptance.
