🤖 AI Summary
This study addresses the persistent blocking of AI agents caused by tool failures triggered by regulatory interventions, which severely impedes task completion. By investigating the interplay between supervisory disruptions and cost-aware backoff strategies within a controlled file recovery environment, this work elucidates the relationship between intervention costs and action blocking. Accordingly, it proposes an adaptive backoff rule grounded in known inducing events, validated experimentally through stochastic policy agents, moving average algorithms, and Gemini 2.5 Flash. The results demonstrate that moderate-intensity backoff strategies significantly reduce blocking frequency and improve task completion rates, outperforming fixed weak-supervision baselines. Ultimately, this research establishes a novel paradigm for optimizing agent robustness against external regulatory interference.
📝 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.