evolve

To update AI agent instructions from accumulated session learnings, you run a gated self-evolution workflow that digests feedback into a controlled PR, ensuring only validated learnings update agent docs.

A gated self-evolution PR for AI agents is a controlled workflow that digests session learnings into structured updates, enforcing validation checks and allowlists to ensure only approved changes merge into agent docs or skills.

You digest session feedback to improve agent documentation by executing an evolution command that clusters undigested learnings by target and yields minimal, relevant edits within a gated PR cycle.

Yes, you can control which AI agent learnings are merged by using an evolution workflow that enforces gate-controlled reviews, allowlists, and validation checks to ensure only approved feedback produces changes.

Yes, the evolution workflow supports an end-to-end flow that handles branching, editing, labeling, and archiving within a single PR cycle for structured ingestion of agent learnings.

Automated agent instruction updates are limited by gated reviews and validation checks, meaning only approved learnings that pass structured ingestion and allowlist constraints produce merged changes.