How to Manage Multiple Coding Agents Without Losing Review Control

Running several AI coding agents in parallel shifts the core bottleneck from compute to review management, requiring structured ownership and evidence tracking. Each task should have a single owner, a defined file scope, and a clear validation command to prevent conflicts from surfacing only at the integration stage. Completed agent sessions should produce a review packet answering what changed, what proves it works, and what could still go wrong — without requiring reviewers to re-read full transcripts. Tasks should be prioritized by blast radius and reversibility, not simply by completion time, so high-risk changes like auth or migrations are reviewed before low-impact edits. Artifact status must remain independent of session status, with explicit states such as ready, under review, accepted, and verified to prevent unreviewed changes from being silently shipped.