Verifying Adaptive AI Agents Through Finite Rule Revision

The deployment of adaptive AI systems in industrial settings faces a significant challenge: verifying their reliability and safety, especially given their non-deterministic nature and limited observability. This research introduces a structured verification protocol specifically designed for adaptive agentic controllers, which are represented by finite symbolic rules, explicit diagnostic predicates, and explanation logs. The core idea is to treat the controller as a revisable object, allowing for the detection and local repair of failures. The protocol maps diagnostic failures to predefined rule-level edits, such as adding, deleting, or revising rule priorities. After a repair, the modified controller is re-evaluated on new simulation scenarios to confirm its effectiveness and ensure it doesn't violate critical thresholds or guardrails. This approach aims to minimize reliance on continuous human-in-the-loop judgment. Experiments conducted in a simulated financial inventory-control environment showcased three types of outcomes: failures that were non-repairable by a single rule edit, partial repairs that were rejected due to constraint violations, and successful one-step repairs for issues like order-volatility. This methodological contribution offers a practical, simulation-compatible framework for making controller failures observable, explainable, and empirically testable under controlled conditions, bridging the gap between prototype and production.