AI outputs like code need execution safety checks beyond model evaluation. A layered architecture of scope analysis, static code review, and runtime monitoring limits risk when AI generates what runs.

Uniform verification either wastes compute on low-stakes queries or under-invests in high-stakes ones. Consequence-driven verification calibrates depth, redundancy, and conservatism to the cost of being wrong.

String matching for knowledge coverage seems simple, and it fails in production the moment terminology varies: a system that knows Universal Gravitation reports zero coverage on gravity. A layered matching architecture fixes both failure directions.

Search-index retrieval finds similar documents. Reasoning needs the logical structure of a domain: principles, concepts, processes, and functions, ingested in dependency order. Lessons from building the Helix learning engine.