AI can write code fast. The real challenge is making that code reliable, secure, testable, and ready for production.

Are you using Claude Code but still dealing with vague prompts, oversized changes, failed tests, weak verification, or unpredictable agent behavior? Do you want AI coding agents to contribute like disciplined engineers instead of generating code you must constantly repair?

Claude Code becomes far more valuable when it works inside a controlled engineering process.

Claude Code Agent Engineering shows you how to design practical AI coding workflows that plan carefully, inspect real repositories, make bounded changes, test results, diagnose failures, and stop when human judgment is required.

Through a connected production-oriented software project, you will learn how to:

• create effective CLAUDE.md instructions and repository rules

• turn product requests into clear, testable engineering tasks

• manage context, permissions, hooks, memory, and agent boundaries

• use subagents, worktrees, MCP servers, and GitHub Actions

• test, debug, review, secure, and verify AI-generated code

• build controlled CI/CD and production delivery workflows

• apply the CONTROL Loop for repeatable agentic software development

The book includes working commands, configurations, code examples, prompts, checklists, verification gates, and practical implementation steps.

Unlike books that focus only on prompting or code generation, this guide treats Claude Code as part of a complete software engineering system. Every stage connects agent actions to repository context, testing evidence, security controls, and human review.

It is written for developers, technical leads, engineering teams, AI coding users, and software professionals who want dependable results from Claude Code and modern coding agents.

Build faster without sacrificing control, quality, or engineering judgment. Get your copy and start creating reliable AI coding workflows today.