Review without Skew

Large language model (LLM) agents can now draft executable business logic — compliance rules, audit checks, detection heuristics — orders of magnitude faster than human engineers. In regulated domains, however, no such logic ships without a human domain expert’s sign-off. We identify a subtle and, in our experience, pervasive failure mode in this human-in-the-loop pattern: review/serving skew, in which the evidence a domain expert reviews is generated by a re-implementation of the candidate logic (a survey script, a notebook, a prompt-level approximation) rather than by the production engine that will ultimately execute it. When the two implementations diverge — and they reliably do — expert approval validates the wrong behavior. We present a discipline that eliminates this failure mode by construction: candidate rules are drafted directly into the production repository in an inactive-but-runnable state, all review evidence is generated by the production execution path, refinement edits the production artifact itself, and activation is reduced to a one-line data change. We describe the discipline in the context of a deployed multi-agent system that authors, validates, and ships declarative compliance rules for automated audit of collision-repair insurance estimates, evaluated against a corpus of approximately 497,000 production documents. The system couples eight specialized LLM agents with deterministic safety rails — golden-fixture regression, an activation allowlist, single-rule change isolation, and corpus-governance constraints — and drives refinement through a bounded, measurable convergence loop against expert false-positive verdicts. We formalize the discipline as five principles, catalog the anti-patterns it forbids, report operational experience from sixteen production rules, and argue that the pattern transfers directly to clinical decision support, financial transaction monitoring, legal document review, and content moderation.