ControlStrong architectural inferencev1.22.1

In plain English

This page explains the governance layer: rules, logs, approvals, signatures, audits, permissions, and rollback tools. These controls are necessary, but they also become important failure points.

  • Why this matters: AI risk can come from the whole arrangement, not one obvious model.
  • What to look for: data, memory, routes, adapters, tools, evaluators, updates, and rollback paths.
  • Technical version below: the expert terminology remains available and is linked through the glossary.

ModelBreeder Architecture Governance Checklist

Evidence levelStrong architectural inferenceTechnical label: Architectural inference

A controlled model-breeding architecture is only useful if the controls are as explicit as the variation loop. This checklist converts the new report intake into reviewable control tasks.

Intake controls

Candidate controls

Evaluator controls

Release controls

v1.22.0 risk-side boundary

Evidence levelStrong architectural inferenceTechnical label: Strong architectural inference

This page preserves possibility-side source material. The A behavior pattern that can survive, move, or reappear across a changing AI system. Open glossary definition use is narrower: translate controlled-evolution vocabulary into risk review. For that translation, use ModelBreeder Risk Side, ModelBreeder Risk Translation, and Model Breeding Risk Boundaries.