AGENTIC AI ENGINEERING

Book Description

Agentic AI is no longer experimental. It is becoming production infrastructure.

As large language models evolve from passive text generators into autonomous, tool-using systems, engineering teams face a new challenge: building agentic AI systems that are reliable, observable, secure, and scalable in real-world environments.

Agentic AI Engineering is a professional, systems-level guide for engineers who want to move beyond demos and prototypes and learn how to design, deploy, and operate production-grade LLM agents and multi-agent systems.

Written for experienced developers, ML engineers, and applied researchers, this book treats agentic AI as an engineering discipline, not a collection of prompts or frameworks. You will learn how modern agentic systems are architected, why they fail in production, and how to build autonomous AI workflows that can be trusted at scale.

Inside, you will explore:

How agentic AI systems evolved from early research to enterprise-grade architectures

Core design principles behind LLM agents, multi-agent coordination, and tool orchestration

Production architectures using LangGraph, LangChain, and Python-based systems

How to implement, debug, test, and monitor agentic workflows in real environments

Techniques for evaluation, observability, cost control, and performance optimization

Common failure modes such as hallucination, prompt injection, tool misuse, and state corruption

Secure deployment patterns, governance strategies, and compliance considerations

Realistic, production-inspired case studies that mirror enterprise workloads

Unlike introductory AI books, this guide emphasizes why systems behave the way they do, how architectural decisions affect reliability, and what it takes to operate autonomous agents continuously in production.

Whether you are building internal AI tools, autonomous assistants, data pipelines, or multi-agent platforms, Agentic AI Engineering provides the depth, rigor, and practical insight needed to engineer systems that perform, recover, and scale.

This book is written for professionals who are ready to treat agentic AI as serious software infrastructure.