Verification of Adaptive Agentic Controllers through Finite Rule Revision

Jul 16, 2026

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Industrial agentic AI systems increasingly face a capability-to-deployment verification gap, especially when adaptive agents generate plausible outputs that remain hard to verify under non-determinism, confidentiality constraints, limited context, and weak observability. This paper proposes a bounded verification protocol for adaptive agentic controllers represented as finite symbolic rules with explicit diagnostic predicates, explanation logs, and held-out re-evaluation. It maps diagnostic failures to finite rule-level edits and tests repaired controllers on held-out simulation seeds or cloned initial states, enabling local repair or rejection without unrestricted human-in-the-loop.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages28

Reading Time1 h 11 min

Introduction

Deployment verification gap

Controller-level verification scope

arXiv :2607 .09770v 1 [ cs .AI ] 7 Jul 2026 Verification of Adaptive Agentic Controllers through Finite Rule Revision Roberto Garrone Open University of Cyprus [roberto. garrone@st. ouc. ac. cy](roberto. garrone@st. ouc. ac. cy) 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. 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? 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. Experiments in a stylized financially constrained inventory-control benchmark show three outcomes: resource-induced failures that remain non-repairable by one rule edit, partial repairs that are rejected because they violate thresholds or guardrails, and a local one-step repair of an order-volatility failure induced by removing a smoothing rule. 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. Keywords: agentic AI; adaptive controller; verification; agent-based simulation; symbolic controller; finite rules; contestability; explanation logs; held-out evaluation; machine coaching. 1 Introduction Agentic AI systems are increasingly used to execute multi-step tasks, coordinate tool use, retrieve contextual information, and adapt their actions to changing operational conditions. These capabilities make them relevant for enterprise automation, decision support, and workflow coordination. However, the transition from prototype demonstrations to production deployment remains limited by a verification problem. Recent empirical evidence on industrial agentic AI adoption identifies a capability-deployment verification gap: organizations may demonstrate higher-level agentic capabilities experimentally while being unable to integrate them into production workflows because adequate output-verification mechanisms are absent [6] . In enterprise settings, a common architectural response is to prevent the agent from becoming the system of record. The agent may interpret tasks, retrieve approved context, select allowed tools, and recommend actions, but execution should remain mediated by governed APIs, workflow engines, RPA layers, enterprise systems, and approval gates. This design principle is necessary because it separates reasoning and coordination from authoritative execution. It isnot sufficient, however, because it does not by itself verify the behavior of the agentic controller. Even when execution is routed through governed systems, the controller may still select inappropriate actions, overreact to noisy signals, fail to respect diagnostic constraints, or produce decisions that cannot be explained and re-tested. Deployment verification gap. In this paper, the deployment verification gap denotes the following technical condition. Let A be an adaptive agentic controller that produces actions at from observed states st under stochastic or context-dependent conditions. A proto

FAQs

What problem does the paper address in industrial agentic AI deployment?

It addresses the capability-to-deployment verification gap, where prototype traces exist but the mapping from controller logic to admissible behavior cannot be inspected, bounded, revised, or re-tested against declared properties.

How are adaptive agentic controllers represented for verification in this work?

They are modeled as finite revisable objects using finite symbolic rules, explicit diagnostic predicates, explanation logs, and held-out re-evaluation.

What kinds of controller failures can the framework detect, repair, or reject?

Diagnostic failures are mapped to predefined rule-level edits such as rule addition, rule deletion, and priority revision; repaired controllers are then evaluated on held-out simulation seeds or cloned initial states to determine whether they are accepted or rejected.

Jul 16, 2026

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