Code That Builds Itself: A Practical Guide to Agentic AI Development Workflows

The developer's role is fundamentally shifting from writing code to directing it. Agentic AI can now read specifications, explore codebases, write implementation code, create tests, fix bugs, and submit pull requests without a developer typing a line of implementation. This practical guide is for engineers who want to master this new paradigm and stay ahead of the curve.

This book provides a framework for moving beyond autocomplete to build a development practice where you and your AI agents create software together. You will learn how to choose the right tools, manage context, and review AI-generated code effectively. It addresses the unique challenges of this new workflow, including quality debt, cognitive decline, and security. Understand the mental models needed to work with LLMs and implement prompt patterns that scale.

Whether you are using Cursor, Cline, or other agentic tools, this book will show you how to set up for success, build multi-agent workflows, and manage costs. It also covers the crucial topics of security, governance, and the cognitive debt paradox. This is the essential resource for developers who want to build software faster and more reliably by forming an effective partnership with AI agents.

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