Overview

Most agent systems are easy to demo and difficult to engineer well. This book is for serious engineers building systems that need to work beyond the prototype. It covers when to use agents and when not to, how to evaluate and harden them, and how to design for reliability, governance, and production reality. 11 chapters. 59,000 words. Zero hype. Just engineering. You'll learn: - When an agent is justified and when a workflow is enough - How to evaluate behavior, failure modes, and production readiness - How to harden systems for deployment, governance, and security - How to build the judgment that separates engineering from demos The book includes working Python code, 36 architecture diagrams, and the kind of honest assessment that only comes from building and operating agent systems in regulated industries. Six mental models recur throughout: the autonomy gradient, the agent tax, earn your complexity, instrument don't hope, the loop is the leverage, and trust boundaries are design decisions. These ideas compound across chapters and give the book its spine. Free online edition at sunilprakash.com/agentic-ai. Source code at github.com/sunilp/agentic-ai.

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