This paper presents a novel multi-agent system called Chat-of-Thought, designed to facilitate the generation of Failure Modes and Effects Analysis (FMEA) documents for industrial assets. Chat-of-Thought employs multiple collaborative Large Language Model (LLM)-based agents with specific roles, leveraging advanced AI techniques and dynamic task routing to optimize the generation and validation of FMEA tables. A key innovation in this system is the introduction of a Chat of Thought, where dynamic, multi-persona-driven discussions enable iterative refinement of content. This research explores the application domain of industrial equipment monitoring, highlights key challenges, and demonstrates the potential of Chat-of-Thought in addressing these challenges through interactive, template-driven workflows and context-aware agent collaboration.
@article{arxiv.2506.10086,
title = {Chat-of-Thought: Collaborative Multi-Agent System for Generating Domain Specific Information},
author = {Christodoulos Constantinides and Shuxin Lin and Nianjun Zhou and Dhaval Patel},
journal= {arXiv preprint arXiv:2506.10086},
year = {2025}
}