Preface

Generative AI and coding agents are rapidly changing how software is built. Tools such as Claude Code, Cursor, and LangChain deep agents are no longer limited to simple prompt-based interactions. They operate across full code bases, integrate with external tools, and execute long-running, multi-step workflows. As these systems become more capable, understanding how they work and how to control them becomes essential.

This book focuses on building a strong and practical understanding of Claude Code, agentic workflows, and the underlying concepts that make modern coding agents effective. A central theme throughout the book is context engineering, which explains how agents construct, manage, and use context as they reason and act. As agents grow more complex and tool-driven, proper context handling directly affects performance, cost, reliability, and consistency.

In this book, you will learn how Claude Code interacts with a code base, how to configure it for daily use, and how to extend it using the Model Context Protocol (MCP). You will explore automation with GitHub Actions, structured planning, multi-agent workflows, subagents, output styles, and agent skills. The book also examines deeper architectural topics, including how deep agents operate and how long-horizon workflows are implemented in practice.

Rather than focusing only on surface-level usage, this book aims to help you understand how these systems are designed and why they behave the way they do. By combining practical examples with deeper architectural insight, the goal is to give you both the skills to use modern coding agents effectively and the understanding needed to adapt as the field continues to evolve.

Who this book is for

This book is for developers and engineers who want to move beyond chat-based AI and build agentic, automated development workflows using Claude Code and MCP. It is intended for application developers with prior software engineering experience who want to integrate context-aware AI agents into their terminal and IDE, automate complex coding tasks, and design scalable multi-agent systems.

It will also benefit AI engineers and generative AI practitioners who work closely with modern development workflows.

To get the most out of this book, you should have experience writing and debugging code in Python or TypeScript, be comfortable working with Git and development environments, and understand core generative AI concepts such as LLMs, RAG, and agents. This is not a beginner-level book and assumes prior software development experience.

What this book covers

Chapter 1, Context Engineering, introduces context engineering and explains why it is critical when building modern AI agents. It covers how context evolves from prompt engineering, how it grows in complex systems, and how poor context handling affects performance and cost. The chapter also examines system prompts and practical context management using Claude code.

Chapter 2, The Gist of Claude Code, provides a practical introduction to Claude Code and explains how it interacts with a code base. It covers project initialization, CLAUDE.md files, permissions, and context management. The chapter also introduces MCP, spec-driven design, and begins building the HookHub project used throughout the book.

Chapter 3, Getting Started with Claude Code – A Tour of Essential Commands, focuses on configuring Claude Code for daily use. It covers pricing and authentication, slash commands, user- and project-level configuration, and integration with Cursor. The chapter also explores hooks, checkpointing, custom commands, and the role of the language server protocol.

Chapter 4, Extending Claude Code with MCP Servers and Plugins, explains the Model Context Protocol and how it standardizes integration between AI applications and external tools. It covers MCP architecture, including hosts, clients, and servers, and demonstrates how to configure local and remote MCP servers. The chapter also addresses context management, performance considerations, and plugin-based extensions.

Chapter 5, Automating Your Development Workflow with Claude Code and GitHub, explores the integration between Claude Code and GitHub. It covers repository configuration, pull request and issue automation, and the use of GitHub Actions for workflow execution. The chapter also explains how YAML workflow files define event-driven automation.

Chapter 6, Claude Code Planning and Multi-Agent Workflows, examines structured agentic workflows using planning and coordinated multi-agent execution. It explains how spec-driven development improves predictability and how multiple agents can collaborate within the same codebase.

Chapter 7, Working with Claude Code Subagents, introduces Claude Code subagents and explains how they enable structured and isolated workflows. It covers custom subagent configuration, context flow, and concurrent execution using the Infinite Agentic Loop pattern.

Chapter 8, Creating and Customizing Output Styles, examines how output styles shape Claude Code's responses. It covers creating and scoping custom styles, structured formats such as YAML, and automating behavior within style definitions. The chapter also explains how to manage roles and session configuration.

Chapter 9, Understanding Agent Skills, explores agent skills as a mechanism for extending AI agent capabilities. It covers foundational concepts, practical usage in Claude Code and LangChain DeepAgent, and the internal context flow when a skill is invoked. The chapter concludes with a real implementation example.

Chapter 10, Using Claude Code Desktop, explains how to use Claude Code within the desktop application. It covers switching between local and cloud modes, running parallel agents with Git worktrees, and coordinating feature branches across environments.

Chapter 11, Understanding Deep Agents, examines deep agents and their role in long-horizon task execution. It defines the characteristics of deep agents and analyzes the LangChain deep agents harness, including its architecture and execution model.

To get the most out of this book

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.

This book includes examples and screenshots adapted from the author's original course materials, which reference repositories created for instructional purposes.

Download the example code files

This book includes a complete downloadable code bundle containing all the example projects and files used throughout the chapters. We recommend downloading the bundle so you can follow along smoothly and experiment with the examples.

Use the bundle as a practical starting point. Modify it, extend it, and apply what you learn by creating your own variations as you progress through the chapters.

Get the code bundle

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4. Upload your proof of purchase and download the code bundle locally

Usage note: You're free to use and modify this code for personal learning and non-commercial projects.

Download the color images

We also provide a PDF file that has color images of the screenshots and diagrams used in this book. You can download it here:https://packt.link/gbp/9781806022595.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and X/Twitter handles. For example: "This is done by initializing CLAUDE.md files so they can be added to the project context."

A block of code is set as follows:

add_context() {
  local context_ref="$1"
  grep -qxF "$context_ref" "$CLAUDE_MD" || echo "$context_ref" >>
    "$CLAUDE_MD"
}

Any command-line input or output is written as follows:

npx create-next-app@latest

Bold: Indicates a new term, an important word, or words that you see on the screen. For instance, words in menus or dialog boxes appear in the text like this. For example: "Select Yes for now, and choose not to be asked again during this session."

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