A Conceptual Design of Industrial Asset Maintenance System by Autonomous Agents Enhanced with ChatGPT

This article introduces OPRA (Observation-Prompt-Response-Action) and its multi-agent extension, COPRA (Collaborative OPRA), as frameworks offering alternatives to traditional agent architectures in intelligent manufacturing systems. Designed for adaptive decision-making in dynamic environments, OPRA enables agents to request external knowledge—such as insights from large language models—to bridge gaps in understanding and guide optimal actions in real-time. When predefined rules or operational guidelines are absent, especially in contexts marked by uncertainty, complexity, or novelty, the OPRA framework empowers agents to query external knowledge systems (e.g., ChatGPT), supporting decisions that traditional algorithms or static rules cannot adequately address. COPRA extends this approach to multi-agent scenarios, where agents collaboratively share insights from prompt-driven responses to achieve coordinated, efficient actions. These frameworks offer enhanced flexibility and responsiveness, which are critical for complex, partially observable manufacturing tasks. By integrating real-time knowledge, they reduce the need for extensive training data and improve operational resilience, making them a promising approach to sustainable manufacturing. Our study highlights the added value OPRA provides over traditional agent architectures, particularly in its ability to adapt on-the-fly through knowledge-driven prompts and reduce complexity by relying on external expertise. Motivational scenarios are discussed to demonstrate OPRA’s potential in critical areas such as predictive maintenance.