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arXiv AI By 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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The paper introduces the Trust Responsive Authorization Policy (TRAP), which translates an agent’s runtime trust, stability, and risk into discrete authorization levels using an asymmetric rule that escalates authority only after consecutive high readings and restricts it immediately on sharp drops. Empirical tests on 100 Monte Carlo traces show TRAP limits authority at the onset of abrupt degradation and re‑escalates faster than symmetric policies, achieving higher stability with lower variability. Sensitivity analyses reveal that recovery time depends on escalation margin and run length, while the policy’s advantage is most pronounced when degradation is abrupt rather than gradual.

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