dispatching-parallel-agents

You can dispatch parallel AI agents to investigate and fix separate failures concurrently. This involves grouping independent failures by domain, assigning each agent a focused task with clear constraints, and verifying all fixes by running the full test suite afterward.

The best way to triage unrelated test failures is to dispatch parallel agents that investigate independent root causes simultaneously. By creating self-contained agent prompts for each domain, you enable concurrent execution and significantly reduce total time to resolution compared to sequential debugging.

You should use parallel agents to debug test failures when there are three or more independent failures across different files or subsystems. This approach is effective when failures can be investigated without shared state and have separate root causes.

Dispatching parallel agents works by identifying independent failures, creating focused tasks with specific scopes and expected outputs for each agent, and running them concurrently. After execution, you review agent summaries for conflicts and run the full test suite for verification.

The limitation of using concurrent agents is that failures must be independent and lack shared state to avoid conflicts. If bugs are interdependent or span the same files, parallel investigations may conflict, requiring sequential debugging and manual conflict resolution during the post-run review.