dispatching-parallel-agents

Running parallel agents for AI automation requires dispatching dedicated agents to handle multiple independent investigations concurrently. This approach isolates each problem domain to prevent cross-contamination, enabling concurrent execution that reduces delivery bottlenecks caused by sequential work on unrelated subsystems.

Parallel agent orchestration is ideal when you have 3 or more unrelated failures or independent subsystems to investigate. You should use it when tasks have clear boundaries, no shared state, and independent scopes, allowing each agent to run concurrently without conflicts and return a focused summary report.

The best way to dispatch agents for unrelated failures is to assign one agent per independent domain. This ensures each problem is isolated for simplified debugging, and each agent returns a defined output summarizing its findings, which can then be reviewed and merged without conflicts.

Coordinating parallel agents that require shared state is not recommended. This dispatch method specifically requires independent scopes and clear task boundaries for each agent to avoid cross-contamination, ensuring tasks can run in parallel without conflicts during execution.

To get started with dispatching parallel agents, simply provide a list of independent tasks. The system will launch dedicated agents to work on them concurrently, with each agent returning a focused report summarizing its findings for your review.