dispatching-parallel-agents

You orchestrate parallel agents by assigning one agent per independent domain, giving each a focused objective and context to work concurrently without leaking history. This reduces total investigation time by ensuring each agent tackles a separate problem domain simultaneously.

Dispatch parallel agents when you have 3 or more test files or subsystems that can run without shared state and sequential dependencies. This approach avoids cross-task interference and aggregates independent analyses efficiently.

Triage failing tests by deploying several focused agents, each dedicated to a separate problem domain, and collect concise summaries. Each agent runs an independent analysis in parallel, aggregating results without cross-task interference.

The best way to assign scoped prompts is to give each agent a focused objective and context without leaking history. This ensures each parallel agent receives a clear goal and outputs a concise summary of findings while avoiding cross-task interference.

Parallel task dispatch is not suited for subsystems with sequential dependencies or shared state. It requires independent tasks that can run concurrently; applying it to dependent tasks causes cross-task interference and prevents proper orchestration.

No, multitask agents across independent domains do not need shared state. Dispatching parallel agents requires tasks to run without shared state to avoid cross-task interference and ensure each agent receives a focused scope.