mindful-hermes-continuous-improvement

Continuous improvement for agent regressions requires capturing the behavior gap, applying a reusable patch, and verifying the fix holds. This loop enforces capture, fix, and verify stages using inspection rather than memory to confirm corrections become permanent behavior changes.

Decision mining classifies recurring signals like regressions, missing capabilities, and janitor findings into actionable categories. It routes these classified signals into the correct review artifacts, ensuring behavior gaps are tracked and queued for explicit patch-or-queue approval.

Patching governing skills requires classifying the behavior gap and applying a reusable fix within explicit approval boundaries. The system uses inspection rather than memory to validate outcomes, ensuring the patch is evidence-backed using improvement-root and pending-ledger paths.

Yes, cron health and workspace hygiene findings are tracked within the weekly review workflow. The system produces compact weekly inspection outputs to show whether operations are truly improving by routing these classified findings into review artifacts.

Journaling errors without a continuous improvement loop results in recording failures without verified behavior change. Without classifying signals and applying evidence-backed verification using improvement-root and pending-ledger paths, corrections risk becoming journaling theater.

Yes, explicit approval boundaries are required to apply reusable fixes for capability gaps. The system enforces a patch-or-queue behavior, meaning fixes are queued for approval before being applied to governing skills and verified using Hermes-native primitives.