Structural measurement layer for monitoring autonomous orchestration risk as a trajectory property, not a technique count (ORDSPOC)

Recent public analysis of AI-enabled intrusion activity reports that the number of distinct techniquesan actor uses no longer tracks the risk that actor poses: the durable dierentiator is the degree towhich a model is scaolded to chain stages of an operation autonomously, with minimal human inter-vention. Technique-inventory frameworks catalog discrete, present-or-absent items; they have no nativerepresentation for the coupling across stages that this risk lives in. This note presents ORDSPOC, asmall, safe, reproducible proof-of-concept that measures autonomous orchestration as a property of thewhole trajectory rather than of its parts. On ve synthetic, fully abstract trajectories it shows thatstage counting and trajectory measurement disagree in a specic, reproducible way: paths with morestages but external (human) gating are ranked above sustained autonomous orchestration by counting,and below it by the structural measure. ORDSPOC is not a calibrated risk model and contains nooperational attack content. The proposed measure introduces an Autonomous Coupling Index (ACI)and trajectory-level viability regimes capable of separating sustained autonomous orchestration fromhuman-gated activity.