CASEL: Consequence-Aware Selective Execution for Reliable Long-Horizon Tool-Using Agents

Authors: Yanyan Zhang , Fei Shen , Ting Yan , Hao Zhang

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Abstract: Tool-using language agents increasingly modify external systems, yet most runtime safeguards allocate scrutiny by generic uncertainty scores or coarse mutation heuristics. We introduce CASEL (Consequence-Aware Selective Execution Layer), a model-agnostic runtime allocation policy that combines calibrated failure probability, state-dependent consequence, reversibility, ambiguity, and evidence freshness to choose, for each proposed tool call, among direct execution, verification, sandboxing, clarification, approval, and abstention by minimizing a per-mode cost. We formalize direct execution as selective prediction and state and test the validity conditions of a finite-sample threshold and a trajectory risk ledger. In a controlled simulation over 20 seeds with 120,000 test actions each, in which every method has the same intervention menu and the same compute cost, CASEL reduces harmful-side-effect trajectories from 14.75 percent for an uncertainty-ranked gate to 7.02 percent and mean normalized harm from 0.184 to 0.123. A gate ranked by the product of failure probability and consequence reaches similar harm (5.82 percent, 0.120) but sends 45 percent more actions to human approval, and removing reversibility-aware routing raises the rate to 15.53 percent. A trajectory-level risk budget, in contrast, increases harm at equal cost (11.82 percent). Intervention effectiveness is assumed, not measured: the results rank allocation rules under the simulator's assumptions and do not establish deployment effectiveness.

Comments:

6 pages, 4 figures. Accepted at the 2026 5th International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE 2026)

Subjects:

Artificial Intelligence (cs.AI) ; Cryptography and Security (cs.CR); Software Engineering (cs.SE)

Cite as:

arXiv:2610.36570 [cs.AI]

(or

arXiv:2610.36570v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.36570

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Ting Yan [ view email ]

[v1]

Tue, 27 Oct 2026 03:19:41 UTC (154 KB)

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