As AI agents move from research labs into production, engineering teams face mounting challenges-fragile tools, rising inference costs, erratic behavior, and unmet expectations. AI Agents: The Definitive Guide addresses what most books avoid: how to design, deploy, and maintain autonomous LLM agents that actually work in the real world. Written by Nicole Koenigstein, a leader in agentic AI with deep experience across finance, research, and engineering, this book offers the practical, system-level foundations needed to build robust, scalable, and secure agentic systems. Whether you're tasked with making agent prototypes production-ready or building mission-critical automation from the ground up, this book guides you through every layer of the stack. It covers architectures, tool integration, performance optimization, safety strategies, and advanced evaluation, with a relentless focus on reliability and long-term value. Design agent systems using modern frameworks like Reflexion, ReAct, and LangGraph Optimize deployment with advanced model techniques and GPU-aware inference strategies Securely integrate agents with tools using sandboxing, API contracts, and interface isolation Build rigorous agent evaluation pipelines and address "agent-washing" failures Scale fault-tolerant agents across orchestration platforms like Ray and Kubernetes Apply debugging and monitoring practices to ensure safe agent reasoning and execution