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

Summary

This paper 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. Using 100 matched Monte Carlo traces, the authors compare TRAP with static, trust-only, risk-only, and symmetric policies. TRAP restricts authority at onset of abrupt degradation and re-escalates sooner after recovery than the symmetric policy, with the highest stability among adaptive policies. However, its restriction advantage depends on degradation being abrupt. Parameter sweeps show recovery time rises with escalation margin and run length, shifts with score weights, and is unchanged by the restriction threshold.

SourcearXiv AIAuthor: Ahsan Habib Shihab, Imre Felde, Ramis Akhmedov, Stefan Gubo, Berik Sabdenaliyev, Madina Mansurova, Amir Mosavi
Trust Responsive Runtime Authorization for Agentic AI with Empirical Characterization of Policy Behavior and Parameter Sensitivity
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[Submitted on 4 Nov 2026]

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

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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.

Comments: 9 pages, 3 figure

Subjects:

Artificial Intelligence (cs.AI)

MSC classes: 68T42 and 68T20

Cite as: arXiv:2611.06996 [cs.AI]

(or arXiv:2611.06996v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.06996

arXiv-issued DOI via DataCite

Submission history

From: Amir Mosavi [view email] [v1] Wed, 4 Nov 2026 18:49:11 UTC (232 KB)

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Key points and analysis

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Key points

  • TRAP uses an asymmetric rule: escalation requires consecutive observations above a band edge plus a margin, while a sharp drop in readiness restricts authority immediately.
  • Under abrupt degradation, TRAP restricts at onset and re-escalates sooner after recovery than the symmetric policy, with the highest and least variable stability among adaptive policies.
  • TRAP's restriction advantage depends on degradation being abrupt; when degradation is spread over multiple intervals, its restriction effectiveness diminishes significantly.
  • Recovery time increases with escalation margin and run length, shifts with score weights, but is unchanged by the restriction threshold.

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