The library-classification model is a clean mental frame for deterministic guardrails. The key strength is that it fails closed — if information doesn't belong on the shelf, movement is physically prevented, not "discouraged" or "flagged for review." That's the right default for anything where a confident-but-wrong action is worse than no action.
The interesting tension is where the strict classifier meets genuinely ambiguous input. A piece of information that's 60% "Mathematics" and 40% something else — does it get rejected, routed to a fallback shelf, or held for adjudication? The classification rule is only as strong as its handling of the boundary cases, because that's where "intelligent" inputs try to slip through. Curious how the engine handles the gray zone between shelves.