dispatching-parallel-agents

To run parallel AI agents, you dispatch dedicated agents to independently investigate separate failure domains concurrently. This approach assigns one agent per subsystem, executing them in parallel to reduce overall investigation time and avoid cross-contamination from sequential analysis.

Use multi-agent parallel execution when you have 3 or more unrelated failures across subsystems with clear domain boundaries and no interdependencies. It is specifically designed to coordinate independent investigations and save time compared to sequential analysis.

Yes, you can track the progress of independent agents running in parallel through centralized status updates. The task-tracking workflow provides progress visibility, showing exactly what investigations are done and what is still pending across all dispatched agents.

The best way to integrate results from parallel AI agents is through structured prompts and a native task-tracking workflow. This supports task creation, parallel execution, and the final integration of independent agent results into a cohesive outcome.

Parallel agent dispatch requires clear domain boundaries to ensure there are no interdependencies between the 3 or more failures being analyzed. This isolation prevents cross-contamination of results and allows each agent to investigate its subsystem independently.