dispatching-parallel-agents

To dispatch independent tasks to parallel agents, allocate each task to a separate agent with isolated context. This prevents shared-state collisions and enables concurrent execution, speeding up resolution by running work simultaneously across different domains.

Use parallel agent orchestration when you have two or more tasks with no shared state or sequential dependencies. Dispatching to parallel agents is ideal when independent problems can be investigated concurrently, allowing faster results compared to sequential processing.

Dispatching parallel agents prevents shared-state collisions by enforcing isolated contexts and constraints for each agent. Keeping each agent's knowledge separate avoids cross-contamination, ensuring safe and coordinated results when investigating multiple independent failures.

Yes, you can use parallel agents to debug multiple module failures concurrently. When failures arise across different modules, dispatch them to separate agents for isolated investigation, then compare their structured outputs before integration to ensure coordinated results.

Parallel agent orchestration is limited to tasks with no shared state or sequential dependencies. If tasks require shared data or must execute in a specific order, dispatching them to parallel agents with isolated contexts is not suitable and may lead to integration conflicts.