The Escape Register: A Failure-Driven Safety Methodology for Multi-Agent Clinical AI Systems

Abstract Multi-agent clinical AI systems introduce failure modes absent in single-model architectures: correlated reasoning errors, stochastic classification of safety-critical signals, and evidence chain failures invisible to output-level evaluation. We present the escape register — a methodology for converting operationally discovered failures into permanent deterministic safety gates within a six-agent clinical pharmacy decision support pipeline. Over 81 executions across three validation blocks, the methodology produced 31 escape IDs, 21 closed gates, and zero safety regressions across three consecutive infrastructure changes. The adversarial review agent concurred with clinical reasoning on an actively toxic narrow therapeutic index case in 37.5% of executions (6 of 16), demonstrating that stochastic agents are unreliable safety classifiers. Post-hoc analysis revealed component-for-component mapping to ASTM F3269-21 — the first such mapping for clinical AI. No published framework combines per-execution failure tracking, deterministic gate conversion, and regression-validated closure in a single lifecycle.