parallel-dispatch

To run parallel AI agents safely, enforce explicit file manifests that guarantee exclusive file ownership for each track. This structured workflow prevents merge conflicts by ensuring agents only modify their assigned files during concurrent refactoring or feature additions.

The best way to verify automated code changes is to run automated build and test checks before integration. This workflow uses git status and test commands to validate each parallel track, re-dispatching any failing tracks after diagnosis to ensure quality.

You can dispatch a maximum of five concurrent AI agents per wave for codebase refactoring. This controlled concurrency limit prevents system overload while allowing multiple independent development tracks to process simultaneously across distinct file sets.

Yes, you can use dynamic routing to assign complex refactoring tasks to advanced models like opus, while routing simpler parallel tracks to models like sonnet. This ensures efficient resource allocation across concurrent development tracks.

When a parallel development track fails automated verification, the error handling process diagnoses the failure and re-dispatches the failing track. This ensures unverified output is automatically corrected before final integration into the codebase.