dispatching-parallel-agents

To execute independent tasks in parallel, you dispatch isolated agents to concurrently solve problems that have no shared state. This pattern groups failures by independent domains, assigns each agent a specific scope, and enforces task boundaries to eliminate cross-task dependencies during parallel execution.

You should use parallel agents for test suites when 3 or more test files fail with different root causes, or when multiple subsystems are broken independently. This pattern applies when problems can be solved in isolation without shared state dependencies across the different test domains.

To dispatch parallel agents for independent investigations, you create focused agent tasks with specific scopes, clear goals, constraints, and expected outputs. You send all agents at once to work concurrently, enforcing independent task boundaries to eliminate cross-task dependencies during the parallel execution.

The best way to review and integrate changes from parallel agents is to read each agent's summary, verify there are no conflicts between the isolated tasks, run the full test suite, and merge the changes. This controlled review process ensures independent fixes do not introduce regressions.

You cannot use this parallel execution pattern for tasks that share state across subsystems. The dispatching parallel agents pattern strictly requires 2 or more tasks with no shared state, as it enforces independent task boundaries and relies on problems being solvable in complete isolation.

The main limitation of dispatching parallel agents is that it only applies when multiple tasks have no shared state and can be solved in complete isolation. If independent problem domains have hidden dependencies or cross-task interactions, the parallel execution pattern will fail to properly coordinate the concurrent fixes.