Traditional predictive maintenance (PdM) systems often suffer from an "execution gap," where predictive insightsremain isolated from the manual coordination required for maintenance actions. This paper explores the transition from isolatedanalytics to collaborative, agentic systems that bridge the gap between prediction and action. By synthesizing advancements inscientific machine learning (SciML) and generative modeling, we establish a high-fidelity predictive foundation for our proposedfour-agent autonomous framework (Insight, Planner, Scheduler, and Communication). We demonstrate how modernorchestration mechanisms, specifically LangGraph, resolve classical Multi-Agent System (MAS) challenges such as sequentialdependency and shared state flow. By integrating intent-based automation and retrieval-augmented reasoning, our frameworktransforms battery monitoring from a diagnostic tool into a system-level autonomous capability, significantly improvingoperational continuity in automotive fleet management.
Kaushik et al. (2026) studied this question.