Agentic AI is redefining predictive maintenance, shifting it from reactive monitoring to autonomous, goal-driven operations. This session explores how an Agentic AI framework can be designed to continuously observe machine signals, reason over both historical and real-time context, and proactively initiate maintenance actions such as work-order creation, spare-parts allocation, and maintenance scheduling.

A critical enabler of this approach is a modern data platform that unifies structured, semi-structured, and unstructured data, allowing AI agents to build a holistic understanding of asset behavior, maintenance processes, and resource optimization. The result is a closed-loop system where insights automatically translate into action-elevating predictive maintenance from isolated analytics to an intelligent, self-learning operational capability.