How a chemical engineer built an agentic manufacturing troubleshooting assistant

Agentic AI troubleshooting assistants are transforming industrial maintenance by enabling domain experts to automate complex data analysis without formal coding experience. By leveraging tools like Claude Code, engineers can integrate historian data, alarm logs, and knowledge graphs to diagnose plant issues, such as low steam temperatures, more efficiently than manual methods. While these systems significantly enhance problem-solving capabilities, they require rigorous human-in-the-loop verification to ensure safety and accuracy. Scaling these prototypes to enterprise-grade solutions involves overcoming significant hurdles, including data quality, security protocols, and the need for standardized benchmarking. Rather than replacing human personnel, this technology shifts the role of engineers from routine firefighting to addressing higher-level operational challenges, ultimately improving plant uptime and energy efficiency. The future of industrial AI lies in bridging the gap between deep domain expertise and technical implementation.