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Chapter 3 · 2026

Agentic AI for autonomous preventive maintenance policy governance: a multi-agent framework for dynamic industrial environments

Adolfo Crespo Márquez, Juan F. Gómez Fernández

Abstract

Agentic Artificial Intelligence (Agentic AI) is emerging as a practical paradigm for coordinating autonomous decision workflows in industrial asset management. This paper proposes an event-driven multi-agent architecture for preventive maintenance (PM) policy governance, implementing a closed-loop cycle that ingests maintenance data, re-estimates reliability under right-censoring, optimizes preventive replacement intervals through deterministic cost–time efficiency evaluation, and produces stakeholder-oriented explanations of the selected policy. The framework has been implemented both as an academic prototype in Python, enabling controlled experimentation under censored and non-stationary conditions, and as a realistic industrial architecture.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

This paper directly addresses one of the core structural challenges in Zone III deployments. The research on Agentic artificial intelligence, preventive maintenance policy governance, multi-agent systems provides evidence-based foundations that enterprise architects cannot ignore when designing long-horizon autonomous workflows. The findings challenge the assumption that a base language model — however capable — can handle the complexity of durable, governed, multi-step execution without explicit architectural intervention. For Zone III practitioners, this paper belongs in the required reading list.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Topics

Agentic artificial intelligencepreventive maintenance policy governancemulti-agent systemscost–time efficiency optimizationmaintenance decision-making