[{"_1":2,"_72":-5,"_73":-5},"loaderData",{"_3":4,"_44":45},"root",{"_5":-5,"_6":-5,"_7":8,"_22":23,"_24":25,"_26":27,"_30":31,"_36":37,"_38":39,"_40":41},"user","shoppingCartId","shoppingCart",{"_9":10,"_11":12,"_13":14,"_15":16,"_17":18,"_19":20,"_21":14},"id","no-cart","count",0,"free",true,"updatedAt","","paymentGateway","STRIPE","cartItems",[],"loading","locale","en","readerTheme","light","constants",{"_28":29},"paypalClientId","AX53Cwz8jR6rVVzzZiZNWWXl0dGagi-GZrjG-S3Ua14E5nrKMn7DxDQ730O4R8LusLPp49ALn6aRVaEy","publishers",{"_32":33,"_34":35},"companies",[],"universities",[],"learnerOrganizations",[],"needsLocaleCookieSync",false,"announcements",{"_42":-5,"_43":-5},"announcement_store","announcement_authors","routes/_store+/_readers+/email_author.$slug/route",{"_46":47,"_71":-5},"book",{"_9":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzc4NzY","title","Building LLM and AI Agent-Based Applications for the Process Industry","state","unpublished","contactAuthorMethod","email","aboutText","

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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