continuous-agent-loop

You orchestrate continuous autonomous agent loops safely by applying quality gates, evals, and recovery controls. This ensures stable long-running AI tasks and maintains session persistence across complex workflows.

Quality gates in autonomous agent workflows are evaluation checkpoints that maintain loop stability. They ensure structured orchestration and safe recovery after errors during long-running AI tasks.

Failure recovery for long-running AI agents is handled using recovery controls that maintain loop stability. These controls allow safe recovery after errors during complex workflow orchestration.

The best way to structure RFC decomposition for AI agents is using an RFC-style DAG loop pattern. This pattern supports complex workflow orchestration with built-in quality gates and session persistence.

Yes, you need a YAML frontmatter with a name and description in a SKILL.md file. You can also use optional components directories like scripts, references, and assets for on-demand execution.

Yes, you can use sequential loop patterns for long-running AI tasks. The loop selection flow supports multiple patterns including continuous-pr, RFC-style DAG, infinite, and sequential workflows.