ultrawork

Parallel execution of independent tasks reduces overall latency by running work items concurrently instead of sequentially. A task graph organizes work into dependency-aware parallel waves, routing each item to specific models for faster turnaround.

A dependency-aware task graph organizes complex multi-step work into parallel waves based on task ordering constraints. This execution orchestration mechanism ensures independent tasks run simultaneously while respecting dependencies, minimizing total completion time.

Parallel task execution supports explicit model routing, allowing independent agents to be assigned to specific models based on task requirements. This routing guidance ensures each concurrent work item uses the appropriate model for its workload.

Background execution is supported for long operations within parallel task workflows. This allows extended independent tasks to run concurrently without blocking other waves, enabling high throughput execution orchestration for complex work.

Parallel task execution provides concise evidence-backed summaries reporting what ran, what changed, and lightweight completion verification status. These summaries track files touched and verification results without adding heavy proof loops or persistence overhead.

Parallel execution orchestration should be avoided when tasks require strict sequential processing, heavy persistence layers, or complex proof verification loops. It is designed for independent work recombination, not tightly coupled dependent workflows needing continuous state tracking.