When the Governor Becomes the Disturbance: Control-Generated Disturbance and Cost-Aware Backoff in Governed Tool-Using Agents

Abstract:Supervisory governors can interfere with the tool-using agents they regulate. We study this possibility in a controlled file-recovery environment where increases in regulatory intensity trigger experimentally imposed tool failures. A cost-blind governor can turn these failures into persistent blocking that prevents task completion. We compare this governor with a backoff rule that reduces intervention probability using a moving average of known induced events. On a hand-coded stochastic-policy agent, the failure pattern appears under both result replacement and execution of corrupted tool arguments. For the persistent policy, adaptive backoff improves completion relative to a fixed weak governor with approximately matched intervention frequency. A Gemini 2.5 Flash experiment comprising 576 episodes across 6 tasks also shows reduced blocking and improved completion under backoff; among the tested settings, intermediate backoff strength achieves the highest observed aggregate success. These results identify an interaction between intervention cost and persistent action blocking, together with a possible mitigation. The cost mechanisms are imposed and their induced events are directly observable to the backoff rule; applicability beyond this controlled environment remains an empirical question.

Includes ancillary experimental data and a Python script for reproducing numerical summaries. Code: this https URL

Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

arXiv:2610.09037 [cs.AI]

(or arXiv:2610.09037v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.09037 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

cost_aware_aggregate.csv

gemini_induced_checkpoint.csv

induced_robustness_baselines.csv

induced_robustness_mechanisms.csv

summarize_results.py

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