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Building LLM and AI Agent-Based Applications for the Process Industry
A gentle introduction to building useful agentic AI industrial solutions
This book is 100% completeLast updated on 2026-04-20
This book familiarizes readers with the world of LLM and agentic AI, and helps them quickly gain a working-level knowledge of building useful agentic AI solutions for process industry operations. With no prerequisites required, practical demo applications, and a hands-on approach adopted throughout, this book makes advanced AI technologies accessible to process engineers and data scientists…
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About
About the Book
This book is designed to help readers quickly gain a working-level knowledge of building LLM-powered and AI agent-based applications tailored for process industry operations. The book covers the complete journey from understanding the fundamentals of Large Language Models and agentic AI to designing, building, evaluating, and deploying reliable industrial solutions. With a hands-on, tutorial-style approach throughout, readers will learn how to interact with LLM APIs, build agents equipped with tools, implement retrieval-augmented generation for plant documentation, orchestrate multi-agent systems, and systematically evaluate their solutions. The application-focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers, and data scientists. Upon completion, readers will be able to confidently build and deploy useful agentic AI solutions for their plants and make informed design decisions suitable for their industrial environments.
The following topics are broadly covered:
· Introduction to LLMs, AI agents, and the agentic AI ecosystem
· Setting up the Python development environment for LLM application development
· Working with OpenAI API and Agents SDK
· RAG, tool use, and agent memory management
· Building and orchestrating multi-agent systems for process operations
· Prompt engineering, structured outputs, and guardrails for production-ready solutions
· Observability, tracing, and systematic evaluation of agentic AI applications
· Demo Applications: Operations Log Assistant; Work-Order Cleaning Assistant; Alert Investigation & Troubleshooting Assistant; Process Data Analyst Assistant
· A comprehensive plant operations assistant for a natural gas processing plant
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About the Authors
Ankur Kumar
Ankur Kumar holds a PhD degree (2016) in Process Systems Engineering from the University of Texas at Austin and a bachelor’s degree (2012) in Chemical Engineering from the Indian Institute of Technology Bombay. He currently works at Linde in the Advanced Digital Technologies & Systems Group in Linde’s Center of Excellence, where he has developed several in-house machine learning-based monitoring and process control solutions for Linde’s hydrogen and air-separation plants. Ankur’s tools have won several awards both within and outside Linde. One of his tools, PlantWatch (a plantwide fault detection and diagnosis tool), received the 2021 Industry 4.0 Award by the Confederation of Industry of the Czech Republic. Ankur has authored or co-authored several peer-reviewed journal papers (in the areas of data-driven process modeling and monitoring), is a frequent reviewer for many top-ranked Journals, and has served as Session Chair at several international conferences. Ankur served as an Associate Editor of the Journal of Process Control from 2019 to 2021, and currently serves on the Editorial Advisory Board of Industrial & Engineering Chemistry Research Journal. Most recently, he was included in the ‘Engineering Leaders Under 40, Class of 2023‘ by Plant Engineering Magazine.
Akhilesh Jain
Akhilesh Jain is an AI leader with a robust interdisciplinary background spanning chemical engineering, computer science, and machine learning. Currently serving as a Product Manager for Advanced AI and Analytics at Baker Hughes, Akhilesh brings over a decade of experience in solving complex industrial challenges across energy, chemicals, and fertilizers industries. Akhilesh is focused on bringing advanced AI capabilities to the process industry transforming health, reliability and optimization. Akhilesh holds a PhD in Chemical Engineering from The University of Texas at Austin, where his research focused on developing software for computational lithography and fluid dynamics.
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Also by the Authors
Also by the Authors
Machine Learning in Python for Process Systems Engineering
Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance
Machine Learning in Python for Dynamic Process Systems
Machine Learning in Python for Visual and Acoustic Data-based Process Monitoring
Knowledge Graphs & GraphRAG for Process Industry
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