Credibility Over Auditability: A Knowledge Graph Enforcement Framework for AI-Assisted Autoverification in CLIA/CAP-Regulated Laboratories

Automated specimen result release (autoverification) is standard practice in high-volume clinical laboratories operating under CLIA and CAP accreditation. The introduction of AI agents into this workflow creates a regulatory credibility gap that existing audit infrastructure is not designed to address: current systems can produce a log of what an AI agent did, but cannot prove the agent was constrained to valid decision paths during execution. We present labintrace, a three-layer enforcement framework that positions a regulatory Knowledge Graph (KG) encoding CLIA/CAP requirements and Westgard QC rules as a hard execution constraint rather than a retrieval source. The framework distinguishes between two fundamentally different audit claims — "no invalid results exist in the final output" (weak, post-hoc) versus "no invalid decision paths were traversed during execution" (strong, provable). The latter is what a CAP inspection requires and what no existing AI autoverification system currently provides. The framework incorporates In-Context Reward Hacking (ICRH) detection adapted from Pan et al. (ICML 2024) to identify AI agents that optimize proxy objectives — such as specimen throughput — at the expense of patient safety constraints. A hash-chained audit trace constructed during traversal, not post-hoc, provides cryptographic proof of the exact decision path executed.