pipeline

You orchestrate multi-stage AI workflows by using a configurable pipeline engine to sequence tasks like planning, execution, and verification. This engine supports state persistence, allowing your AI workflows to successfully resume operations after any unexpected interruptions.

Building an autopilot workflow for feature development is best achieved by defining a sequence of stages for planning, execution, and verification. You can programmatically configure these stages to enable parallel team workers and iterative quality checks.

Yes, you can configure AI pipelines programmatically without hardcoding stages. The pipeline engine provides stage creators and a simple API-driven configuration system, allowing you to dynamically compose and run workflows at runtime.

Pipeline state persistence handles interruptions by saving the current progress of the multi-stage workflow. When an interruption occurs, the engine resumes the pipeline from the last saved state, ensuring no prior execution context is lost.

The limitations of using a multi-stage pipeline for team coordination include the dependency on consistent interfaces across stages. If a stage like team-exec fails verification, the pipeline requires explicit configuration to handle retries or iterative checks properly.