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[CS.AI] Verification of Adaptive Agentic Controllers

Published at: 2026-07-14 22:00 Last updated: 2026-07-15 01:59
#algorithm #AI #Machine Learning

Abstract

Industrial agentic AI systems increasingly exhibit a gap between prototype capability and production deployment. In particular, adaptive agents may generate plausible outputs while remaining difficult to verify under non-determinism, confidentiality constraints, limited context, and weak observability. This paper formulates a bounded verification protocol for adaptive agentic controllers represented by finite symbolic rules, explicit diagnostic predicates, explanation logs, and held-out re-evaluation.

Research Question

The central research question is: when an adaptive agentic controller is represented through finite rules, explicit diagnostic predicates, explanation logs, and held-out re-evaluation, which classes of controller failure can be detected, locally repaired, or rejected without relying on unrestricted human-in-the-loop judgment?

Verification Framework

The proposed framework treats the controller as a finite revisable object. Diagnostic failures are mapped to predefined rule-level edits, including rule addition, rule deletion, and priority revision. Repaired controllers are then evaluated on held-out simulation seeds or cloned initial states.

Experimental Results

Experiments in a stylized financially constrained inventory-control benchmark show three outcomes:

  1. Resource-induced failures that remain non-repairable by one rule edit;
  2. Partial repairs that are rejected because they violate thresholds or guardrails;
  3. A local one-step repair of an order-volatility failure induced by removing a smoothing rule.

Methodological Contribution

The contribution is methodological and provides a simulation-compatible procedure for testing whether specific controller-level failures can be made observable, explainable, locally revisable, and empirically re-tested under controlled conditions.

Blogger's Review: The proposed verification protocol offers a new perspective on ensuring the reliability of adaptive agentic controllers, especially in complex environments, highlighting the significance of effective local repairs in both theoretical and practical contexts.

Original Source: https://arxiv.org/abs/2607.09770

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