Large Language Model (LLM)-based agentic systems present new opportunities for autonomous health monitoring of industrial systems in sensor-rich environments. This study investigates the potential of LLM agents to diagnose faults directly from raw sensor data, while producing inherently explainable outputs through natural language reasoning. Such explainability enables users to interpret and audit agent decisions and confidence levels with greater transparency. We begin by systematically analyzing how different agent configurations, such as centralized versus distributed agent setups, the ability to use computational tools for fault discovery, and the structure and scope of sensor input data impact fault detection accuracy and uncertainty estimation. Building on these findings, we then explore whether LLM agents can improve diagnostic performance over time through continual learning, calibrating their confidence based on historical ground truth outcomes. Through simulation-based experiments across varied degradation scenarios, this work aims to assess the feasibility of LLM-based agents as a foundation for transparent, adaptive fault diagnosis in real-world systems.
Lee et al. (2025) studied this question.