Purpose-built AI respondents can defeat most quality checks, yet a cross-platform study found almost none outside MTurk. Nonetheless, the field lacks scalable detection metrics. This session introduces a first-of-its-kind framework for evaluating detection methods at scale. Scripted bots, commercial agents, and optimised custom builds complete the same instrument as verified humans, and each proposed check is scored by its catch rate while flagging almost no real respondents. The results show which checks to deploy, retire, and layer.