dispatching-parallel-agents

To run parallel agents for independent tasks, you dispatch one agent per problem domain to investigate and resolve issues concurrently. This approach enforces parallel execution without shared state, returning concise, domain-scoped summaries for integration.

You should use parallel agent dispatch when you have two or more independent failures or tasks that can be investigated and resolved concurrently across separate domains. It is specifically designed for unrelated problems that require no shared state.

You consolidate outputs from parallel agents through a structured review workflow. After parallel execution completes, the process enforces structured outputs from each domain and provides a consolidated review to integrate the domain-scoped summaries.

You cannot use this parallel agent pattern for tasks that share data across domains. It is explicitly built for unrelated problems with no shared state, enforcing one independent agent per domain to prevent cross-domain data conflicts.

The best way to distribute tasks across separate AI agents is to assign one agent per independent problem domain. This task distribution method enables parallel investigation and resolution, ensuring each domain receives a dedicated agent.

Parallel agent dispatch does not work for sequential dependent failures because it requires tasks to be independent. It enforces parallel execution across separate domains, making it unsuitable for problems requiring shared state or sequential resolution.