Trust Responsive Runtime Authorization for Agentic AI with Empirical Characterization of Policy Behavior and Parameter Sensitivity

A new arXiv paper (2611.06996v1) empirically characterizes the Trust Responsive Authorization Policy (TRAP), which maps an Authorization Readiness Score combining runtime trust, operational stability and contextual risk onto discrete authorization levels via an asymmetric rule: escalation requires consecutive observations above a band edge plus a margin, while a sharp readiness drop restricts authority immediately. On 100 matched Monte Carlo traces, TRAP restricted at onset under abrupt degradation and re-escalated sooner after recovery than a symmetric policy, with the highest and descriptively least variable stability among adaptive policies, though more of its post-recovery escalations fell below the selected trust threshold. In an exploratory post-freeze experiment spreading the same degradation over 5-20 intervals, TRAP restricted only on the 4 traces retaining a single-interval drop above the threshold and held 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, showing its restriction advantage depends on degradation being abrupt.

arXiv:2611.06996v1 Announce Type: new Abstract: The policy that converts runtime estimates of an autonomous agent's condition into operational authority is rarely specified or measured. We empirically characterize the Trust Responsive Authorization Policy TRAP , which maps an Authorization Readiness Score ARS , a weighted combination of runtime trust, operational stability, and contextual risk, onto discrete authorization levels through an asymmetric rule: escalation requires consecutive observations above a band edge plus a margin, while a sharp drop in readiness restricts authority at once. On one matched set of 100 Monte Carlo traces, we compare TRAP with static, trust-only, risk-only, and symmetric policies. Under abrupt degradation, TRAP restricts at onset and re-escalates sooner after recovery than the symmetric policy, with the highest and, descriptively, least variable stability among the adaptive policies, although more of its post-recovery escalations occur below the selected trust threshold. Paired bootstrap intervals exclude zero for the remaining differences. In one-factor sweeps, recovery time rises with the escalation margin and run length, shifts with the score weights, and is unchanged by the restriction threshold. In an exploratory post-freeze experiment, when the same degradation is spread over 5-20 intervals, TRAP restricts only on the 4 traces that retain a single-interval drop above that threshold; averaged over all 100 traces, it holds authority above what readiness supports for 15.9-18.9 of the 20 degradation intervals, against 0.8 under abrupt degradation and about 10 for the symmetric policy. Its restriction advantage therefore depends on degradation being abrupt. All reported values are traceable to the publicly available, versioned research release.