orchestrating-swarms

A multi-agent swarm orchestrates parallel code reviews by defining Leader, Teammates, and Task roles with dedicated inboxes, allowing multiple agents to process review tasks simultaneously and compress overall delivery time.

Dependency-aware task pipelines automatically unlock downstream work when predecessor tasks finish, using self-organizing teams and APIs to manage task queues so parallel agents execute sequentially only when their dependencies are resolved.

Yes, multi-agent swarms are designed for parallel analysis on large codebases, using self-organizing teams that spawn, coordinate, and gracefully shut down across review cycles to handle high-volume workloads efficiently.

The best way to structure parallel agent orchestration is by defining explicit roles—Leader, Teammates, Tasks, and Inboxes—managed via APIs like TaskCreate and Teammate to establish clear communication and task boundaries.

No external dependencies are required to orchestrate parallel code-review tasks; the system relies on built-in roles and APIs like TaskCreate, TaskUpdate, and Teammate to manage environment details and spawn backends natively.

A self-organizing task queue eliminates manual juggling of teammates and workflows, reducing miscommunication risk and accelerating feedback loops by automatically coordinating parallel agents and managing graceful shutdowns across review cycles.