evolution-engine

Automated rule verification applies checks at session start, during user corrections, and upon task completion to ensure learned rules stay accurate. It uses hypothesis-driven observations and machine-checkable patterns to refine guidance automatically.

Autonomous rule learning captures new patterns and adds them to the rule set with verification hooks. Guardrail enforcement ensures these rules operate within defined safety and scope constraints to maintain accurate guidance.

Run an initial verification sweep at session start to automatically surface rule violations with minimal noise. This automated check identifies and verifies learned rules to maintain accurate guidance during interactive work.

Hypothesis-driven observations record testable claims and confirm or refute them with evidence. This mechanism ensures rules are verified through machine-checkable patterns before being added to the active rule set.

Corrections integration updates rules in response to user feedback to improve system accuracy. It applies these corrections during interactive work to ensure rules stay accurate and aligned with defined guardrails.

Use autonomous learning when you need to identify, verify, and refine rules to maintain accurate guidance during interactive work. It is essential when robust guardrails and machine-checkable verification patterns are required.