dispatching-parallel-agents

You can debug multiple independent test failures simultaneously by dispatching parallel agents, assigning each agent an isolated context to investigate a separate failure domain without cross-contamination.

Parallel agent dispatching is a workflow technique that assigns independent tasks to dedicated AI agents, enabling parallel execution while enforcing structured prompts and isolated contexts to prevent session history leakage.

Yes, you can use parallel execution to diagnose failures across separate subsystems by dispatching one agent per independent problem domain, which is ideal for scenarios with three or more independent failures.

To prevent context leakage when running multiple AI agents in parallel, dispatch each agent with a self-contained prompt and an isolated context, ensuring no shared session history contaminates the independent investigations.

You should use parallel agents instead of a single agent when you have three or more independent failures across test files or subsystems that can be investigated without requiring shared context.

A limitation of dispatching parallel agents is that it applies only to independent failure domains that can be investigated without shared context, making it unsuitable for debugging tasks with interdependent subsystems.