This is the code repository for Agentic Coding with Claude Code, First Edition, published by Packt.
Eden Marco
Most developers encounter Claude Code through chat-style prompting, but that approach breaks down as projects grow and automation must be safe, repeatable, and controlled. Agentic Coding with Claude Code shows how to move beyond ad hoc prompts and use Claude Code as an extensible, agent-driven development platform.
This book focuses on building context-aware AI workflows directly in your terminal and IDE. You will learn how to control Claude Code using slash commands, manage long-term context with persistent memory files, and automate development tasks using hooks that trigger actions across Claude Code's lifecycle.
The book also covers the Model Context Protocol (MCP) as an important part of the modern agentic ecosystem. You will understand why MCP exists, explore its core architecture, and configure MCP servers inside Claude Code to improve context sharing across tools, agents, and workflows. The trade-offs between MCP, skills, and subagents are discussed to help you choose the right approach.
You will design and orchestrate multi-agent systems using subagents, parallel sessions, and hierarchical delegation. By the end of the book, you will be able to integrate Claude Code into real-world development workflows with confidence and control.
- Design agentic coding workflows in the terminal and IDE using Claude Code
- Build custom automations with reusable slash commands and hooks
- Use Claude Code with a Next.js project to implement AI-driven workflows
- Create persistent AI memory using Claude Code memory files
- Apply MCP for structured context sharing across tools and agents
- Design multi-agent systems using subagents and orchestration patterns
- Enforce coding standards using project documentation and context control
- Scale AI pair programming while keeping code maintainable
- Context Engineering
- The GIST of Claude Code
- Getting Started with Claude Code – A Tour of Essential Commands
- Extending Claude Code with MCP Servers and Plugins
- Automating Your Development Workflow with Claude Code and GitHub
- Claude Code Planning and Multi-Agent Workflows
- Working with Claude Code Subagents
- Creating and Customizing Output Styles
- Understanding Agent Skills
- Using Claude Code Desktop
- Understanding Deep Agents
This book assumes prior experience with software development and generative AI concepts. Before you begin, you should be comfortable writing and debugging code in Python or TypeScript, running programs from the terminal, and working with a code base. Basic Git knowledge, such as cloning repositories and committing changes, is expected. You should also understand virtual environments and environment variables.
Familiarity with large language models (LLMs) and core concepts such as agents, RAG, and ReAct is required. You should have previously interacted with an LLM and built at least a simple agent. This book does not cover beginner-level programming or introductory generative AI topics.
To follow along with the hands-on examples, you will need a working development environment with Node.js and a simple Next.js setup, as introduced in the early chapters. You will also need access to Claude Code, along with the appropriate authentication and pricing configuration. Some chapters require installing and configuring the GitHub CLI, working with GitHub repositories, and using GitHub Actions. Later sections involve configuring MCP servers, both local and remote, and running Claude Code within Cursor and the Claude desktop application.
It is recommended that you follow the examples step by step and experiment with the configurations as they are introduced. The book builds progressively, and several chapters rely on projects and setups established earlier, including the HookHub project.
Disclaimer
This book is an independent publication and is not affiliated with, endorsed by, sponsored by, or officially associated with Anthropic, PBC, or any of its subsidiaries or affiliates. "Claude," "Claude Code," and "Anthropic" are trademarks or registered trademarks of Anthropic, PBC. All other trademarks mentioned herein are the property of their respective owners.
The author is an employee of Google LLC. However, this book is a personal project and does not represent the views, opinions, or official positions of Google LLC, Google Cloud, Alphabet Inc., or any of their subsidiaries or affiliates. This book is not endorsed by, sponsored by, or officially associated with Google in any capacity. "Google," "Google Cloud," and related marks are trademarks of Google LLC.
The content in this book is based solely on the author's personal experience, independent research, and publicly available documentation. The views, opinions, and interpretations expressed are those of the author alone and do not represent the official positions, strategies, or opinions of Anthropic PBC, Google LLC, or any other organization.
While every effort has been made to ensure the accuracy and completeness of the information presented, the author makes no warranties or representations, express or implied, regarding the completeness, accuracy, reliability, or suitability of the content. AI tools and their associated APIs, features, and functionalities evolve rapidly, and information in this book may become outdated after publication.
The author and publisher shall not be held liable for any damages, losses, or consequences arising directly or indirectly from the use of or reliance on the information contained in this book. Readers are encouraged to consult Anthropic's official documentation at docs.anthropic.com for the most current and authoritative information.
Eden Marco is an LLM specialist at Google Cloud and a LangChain Ambassador with years of experience in software engineering and cloud architecture. He was one of the first engineers at Orca Security, holds a bachelor's degree in computer science from the Technion, and has taught at Reichman University. Eden creates practical, production-ready courses based on real-world experience.
- Page 32: In Chapter 2, the heading "Using pec-driven design" should be "Using spec-driven design".