dispatching-parallel-agents

Running independent experiments in parallel requires dispatching separate subagents via multiple Task tool calls in a single message. Each subagent operates in isolation without shared state, executing concurrent analyses to eliminate idle time and maximize throughput.

Parallel agent orchestration is the process of spawning multiple subagents simultaneously to handle non-dependent analyses. It coordinates concurrent execution of independent tasks, such as evaluating baselines on different datasets, and integrates results without conflicting IO.

Yes, parallel dispatch supports evaluating multiple baselines on different datasets simultaneously. You can assign one agent per independent problem domain to run experiments concurrently, then integrate the results through coordinated execution and review panels.

Scalable orchestration can handle 3 or more concurrent experiments without shared state. It coordinates parallel execution by isolating each agent, applying quality control gates, and integrating results to prevent conflicting IO during concurrent task processing.

You should not use parallel agents when tasks have shared state or dependencies between them. This approach requires each agent to operate in complete isolation, making it unsuitable for sequential workflows where one experiment relies on the output of another.

To dispatch parallel agents, call multiple Task tools within a single message to spawn subagents concurrently. Assign each subagent an independent task domain, let them execute in isolation, and the system will coordinate execution and integrate the results.