AI coding agents can generate code in seconds, but without the right engineering practices, they can also introduce bugs, technical debt, and inconsistent code. This book teaches you how to build reliable AI-assisted development workflows that scale from individual projects to entire teams, regardless of which coding assistant or model you use.




Rather than treating AI as a black box, you'll learn how AI coding agents work, why they succeed or fail, and how to build systems that consistently produce better results. You'll explore practical methods such as context and harness engineering, along with reusable frameworks such as AEMM, Elastic Loop, and Context Diseases, which you can apply to any development workflow.




You'll also learn how to write better specifications, use AI-TDD to improve code quality, build development harnesses that guide AI agents, and choose the right level of autonomy for different tasks. The book also covers AI security, team adoption, and how to scale from individual AI-assisted development to a software factory where multiple AI workflows work together.




By the end of this book, you'll understand how to move beyond vibe coding and adopt a disciplined, practical approach to AI-driven software development.