dispatch-multiple-agents

To run parallel agents for independent tasks, you dispatch disjoint tasks to separate subagents concurrently. This reduces total execution time and avoids bottlenecks by ensuring unrelated tasks with no shared state are processed simultaneously.

Orchestrating parallel workflows with multiple agents involves identifying unrelated tasks, creating subagents with clear scopes, dispatching them in parallel, and integrating the collected subresults into a final synthesized outcome.

You should use parallel task distribution when your tasks are completely unrelated, have no shared state, and execution speed matters. It maximizes throughput by allowing independent operations to run concurrently without creating bottlenecks.

You scope subagents for parallel execution by providing each one with a precise scope and a defined result format. This ensures each parallel agent understands its boundaries and produces structured subresults for integration.

Dispatching parallel agents is not suitable if your tasks share state. The pattern strictly applies when tasks are unrelated and independent, as shared state prevents safe concurrent execution and disrupts result integration.

To integrate results from multiple parallel agents, you collect the defined subresults from each concurrent subagent and synthesize them into a final outcome. This orchestration step ensures all parallel outputs are combined correctly.