From Condition Visibility to Coordinated Action: An Agentic AI Framework for Transformer Asset Management

Transformer asset management is increasingly shaped by fragmented data environments, variable operating conditions, aging infrastructure, and rising expectations for reliability, explainability, and timely maintenance response. In many utility environments, scheduled diagnostic assessments, live operational monitoring, and maintenance history remain distributed across separate systems, limiting the ability to convert condition visibility into coordinated action. This paper presents a technical framework for transformer asset management built around four coordinated intelligence layers: Periodic Condition Index, Real-Time Monitoring, Maintenance Records, and Predictive Planning. Within this framework, the Asset Health Index (AHI) is treated as an integrated health view rather than an isolated score, preserving standardsbased baseline interpretation while incorporating operational and maintenance context. The paper distinguishes between an AI/ML analytics layer, which supports anomaly detection, pattern learning across the transformer asset base, component-oriented risk attribution, and explainable prioritization, and an Agentic AI layer, which embeds those insights in a bounded operational decision loop for recommendation generation, planning support, and governed next-step guidance under human oversight. The proposed framework is intended to provide a technically grounded path from isolated diagnostics toward auditable, explainable, and proactive transformer decision support in multi-vendor transformer asset environments with varying operational and data maturity.