dispatching-parallel-agents

Parallel agent dispatching assigns one AI agent to each independent failure across different files or subsystems, running investigations simultaneously. This reduces total resolution time compared to sequential debugging, especially effective for 3+ unrelated problems with no shared state.

Use parallel agents when you have 3+ failures across different domains or subsystems with no shared state dependencies. Sequential debugging wastes time when problems are truly independent; parallel processing accelerates resolution by tackling each concurrently.

Structure each agent's task with clear, self-contained goals scoped to one problem domain. This isolation prevents conflicting changes. Review all fixes before integration and validate that each addresses its specific subsystem without overlap.

Parallel agent dispatching works best for independent failures with no shared state. If problems share dependencies or common code paths, sequential investigation or a single agent reviewing the shared component reduces conflicting fixes and wasted parallel work.

Dispatch one agent per unrelated test failure across different subsystems to investigate and fix concurrently. This approach drastically cuts debugging time compared to sequential resolution when 3+ independent problems exist.