dispatching-parallel-agents

To run parallel agents for independent tasks, you assign each unrelated problem domain to a separate agent. This isolates context and prevents cross-contamination while the agents work concurrently to investigate failures.

Dispatch parallel agents when you have 3 or more unrelated failures across different subsystems. This approach allows you to investigate concurrently without sharing state, preserving clear ownership of each independent domain.

The best way to prevent state cross-contamination in multitasking AI workflows is to dispatch parallel agents with isolated context. Assigning one agent per independent domain ensures clear ownership and prevents shared state issues.

Coordinate task distribution by identifying independent domains, creating focused agent tasks with clear scope, and dispatching them in parallel. You then aggregate the results while preserving the boundaries of each domain.

A limitation of dispatching parallel agents is that it only applies to scenarios with independent tasks. It is not suitable for tasks requiring shared context, as the mechanism relies on isolating state to prevent cross-contamination between agents.

No, you do not need shared context to dispatch parallel agents. The mechanism is designed for independent tasks, assigning one agent per domain to investigate failures concurrently without sharing state.