Agentic AI Framework for Autonomous Predictive Maintenance in Electric Vehicles

Traditional predictive maintenance (PdM) systems often suffer from an "execution gap," where predictive insights
remain isolated from the manual coordination required for maintenance actions. This paper explores the transition from isolated
analytics to collaborative, agentic systems that bridge the gap between prediction and action. By synthesizing advancements in
scientific machine learning (SciML) and generative modeling, we establish a high-fidelity predictive foundation for our proposed
four-agent autonomous framework (Insight, Planner, Scheduler, and Communication). We demonstrate how modern
orchestration mechanisms, specifically LangGraph, resolve classical Multi-Agent System (MAS) challenges such as sequential
dependency and shared state flow. By integrating intent-based automation and retrieval-augmented reasoning, our framework
transforms battery monitoring from a diagnostic tool into a system-level autonomous capability, significantly improving
operational continuity in automotive fleet management.