This work investigates a policy that converts runtime estimates of an autonomous agent’s condition into operational authority—the Trust Responsive Authorization Policy (TRAP). TRAP maps an Authorization Readiness Score (ARS), a weighted combination of runtime trust, operational stability, and contextual risk, onto discrete authorization levels using an asymmetric rule: escalation requires consecutive observations above a band edge plus a margin, while a sharp drop in readiness instantly restricts authority. We evaluated TRAP on a matched set of 100 Monte Carlo traces against static, trust‑only, risk‑only, and symmetric policies. Under abrupt degradation, TRAP restricts authority at the onset and re‑escalates sooner after recovery than the symmetric policy; among adaptive policies it shows the highest and least variable stability, although more post‑recovery escalations occur below the chosen trust threshold. Paired bootstrap intervals exclude zero, indicating significant differences. One‑factor sweeps reveal that larger escalation margins and longer run lengths increase recovery time, score weight shifts the recovery point, and the restriction threshold does not affect recovery time. In an exploratory post‑freeze experiment, when the same degradation is spread over 5–20 intervals, only the 4 traces with a single‑interval drop above the threshold trigger restriction; averaged over all 100 traces, TRAP maintains authority above what readiness supports for 15.9–18.9 of the 20 degradation intervals, versus 0.8 under abrupt degradation and about 10 for the symmetric policy. Thus, TRAP’s restriction advantage depends on degradation being abrupt. All reported values are traceable to the publicly available, versioned research release.
Review: TRAP’s asymmetric escalation and immediate restriction provide faster convergence and recovery in sudden‑risk scenarios, highlighting its practical relevance for dynamic safety‑critical authorization.