autonomous-loops

You can automate multi-step tasks by enabling autopilot mode to let agents execute workflows end-to-end with minimal manual input. This coordinates autonomous agents to handle complex backend automation, reducing manual micromanagement and accelerating development.

Parallel workload orchestration coordinates multiple agents simultaneously to accelerate large pipelines. You need it when running multi-step tasks across large codebases, where sequential processing creates bottlenecks and manual coordination becomes unmanageable.

Yes, you can run several agents simultaneously using the /fleet parallelism workflow. This allows you to initiate parallel task runs with clear objectives, executing multiple workloads at once to accelerate large automation pipelines.

Autonomous workflows enforce deterministic task orchestration with built-in validation points and retry handling. These safety mechanisms ensure reliable outcomes by tracking progress and validating steps automatically during end-to-end autopilot execution.

Yes, autonomous task orchestration works without external dependencies. It coordinates agent workflows natively, using built-in progress tracking, validation hooks, and clear operator prompts to ensure safety and traceability throughout the automation pipeline.

Autopilot mode requires clear objectives and operator prompts to maintain safety and traceability. While it minimizes manual input, deterministic task orchestration still relies on predefined validation points and retry hooks, meaning ambiguous goals may cause execution failures.