The Assurance Freshness Gap: Risk-Bounded Revalidation and Queue-Aware Execution Control for Tool-Using AI Agents

Abstract

Tool-using artificial intelligence agents increasingly act on evidence that can change between verification and execution. Existing output-focused assurance and permission gates do not by themselves establish that previously checked preconditions remain valid at action time. This paper develops the assurance freshness gap, a temporal assurance framework combining signed evidence receipts, private and shared-source change hazards, probabilistic revalidation, queue-sensitive risk and conditional atomic commit. Under independent baseline-error and generating-shock assumptions, the paper derives an exact invalid-execution probability and evidence lifetime; a conservative union-bound certificate applies to enumerated dependent events with defensible marginal bounds. A stationary M/M/1 authorization queue yields a 5%-budget utilization ceiling of 85.93% under explicit illustrative parameters. The revision extends the analysis to nonstationary hazards and general M/G/1 service-time distributions. At identical 85% utilization and mean service capacity, theoretical invalid-execution risk is 2.99% with deterministic service, 4.73% with exponential service and 9.48% with high-variance hyperexponential service. Independently replicated synthetic queue simulations corroborate these transform predictions; age-dependent hazard and uncertain-hazard calculations further show how nominally acceptable mean risk can breach the target. The paper provides executable controller pseudocode, formal proofs, sensitivity results and reproducible scripts, while distinguishing conditional probabilistic guarantees from atomic state-transition protection. The evaluation is entirely synthetic and does not establish real-world safety effectiveness.