dispatching-parallel-agents

To run parallel agents for independent tasks, you dispatch one dedicated agent per problem domain to work concurrently without shared state or sequential dependencies, avoiding cross-domain interference. The skill returns concise summaries for each domain for integration.

You should dispatch multiple agents when dealing with multi-failure or multi-subsystem scenarios where tasks have no shared state or sequential dependency, enabling concurrent investigations that save time compared to sequential processing.

You set up parallel agents by assigning one agent per independent domain, providing a structured prompt for each investigation, and collecting concise domain-scoped summaries suitable for integration and further action.

No, this approach applies specifically to scenarios with multiple unrelated failures or subsystems where each problem can be investigated independently. Tasks with shared state or sequential dependencies are not suitable for this parallel dispatch model.

The best way to coordinate AI agents across different problem domains is to enforce one agent per domain to avoid cross-domain interference, provide structured prompts, and return concise domain-scoped summaries for integration.

No, dispatching parallel agents requires no specific dependencies or components. You only need multiple independent tasks or subsystems to investigate, and the skill handles the coordination and structured prompt dispatch automatically.