dispatching-parallel-agents

You can dispatch parallel agents to tackle multiple independent tasks concurrently, which reduces overall remediation time and avoids bottlenecks by assigning each isolated domain to a dedicated agent.

Parallel workflow orchestration is needed when you have 3 or more independent failures or subsystems that can be investigated in isolation without shared state, allowing concurrent task dispatch.

Before dispatching parallel agents, you need a clear scope per task, a defined success criterion, and a specified output format for per-domain summaries to ensure safe integration later.

After dispatching parallel agents to separate issues, you aggregate per-domain results and facilitate safe integration by tracking progress and combining the summaries from each dedicated agent.

The best way to coordinate multiple AI agents on separate test files is to dispatch them concurrently to fix independent issues, such as assigning one agent per failing test file to work in parallel.

You should not use parallel task dispatch for subsystems with shared state because this approach requires that failures or subsystems can be investigated in complete isolation without shared dependencies.