Generative AI Multi-Agent System with Retrieval Augmented Generation for Real-Time Furnace Operation Support in Industrial Manufacturing

This paper presents a prototype AI-powered decision support system that leverages large language models (LLMs) and digital agents to assist furnace-stage operations in transformer manufacturing. The system integrates real furnace sensor data with synthetic manuals and expert insights using a modular architecture that combines Retrieval-Augmented Generation (RAG), structured prompting, multi-agent coordination, and response validation. It processes natural language queries to generate context-aware responses grounded in process data and documentation, demonstrating the potential of domain-adapted generative AI for industrial support. Experiments show a 100% improvement over a baseline LLM, though performance remains 29% below ChatGPT-4o, indicating both promise and areas for future improvement.