ultrawork

Parallel execution coordinates independent tasks concurrently so high-throughput workflows complete faster. It automatically identifies independent work items and dispatches them simultaneously, then runs dependent items sequentially after their prerequisites finish to prevent race conditions.

Dependency-aware sequencing prevents race conditions by running dependent tasks only after their prerequisites complete. This ensures multi-agent task delegation executes dependent items sequentially, maintaining data integrity while firing all independent items concurrently for maximum throughput.

Background operations like builds and tests can run concurrently with other tasks. The system starts long-running actions in the background and uses completion verification to check success when all parallel tasks finish, ensuring reliable workflow orchestration.

Handling mixed sub-items requires a fire-all-independent-items policy that dispatches concurrent work immediately. Dependent items are queued and executed sequentially after prerequisite results are available, optimizing throughput without risking race conditions.

Parallel execution supports multi-agent model routing by coordinating delegated work items concurrently. It identifies independent efforts across agents and dispatches them simultaneously, while sequencing any dependent tasks that require prerequisite results before execution.

Avoid parallel execution when tasks share strict sequential dependencies without independent items to run concurrently. If all work items require prerequisite results from previous steps, the coordinated concurrent dispatch provides no throughput benefit over standard sequential processing.