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Agentic Coding with Claude Code

Agentic Coding with Claude Code

By : Eden Marco
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Agentic Coding with Claude Code

Agentic Coding with Claude Code

4 (2)
By: Eden Marco

Overview of this book

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.
Table of Contents (18 chapters)
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Part 1: Foundations of Context Engineering and Claude Code
5
Part 2: Extending Claude Code: Protocols, Automation, and Structured Workflows
11
Part 3: Advanced Agents and Deep System Design
16
Other Books You May Enjoy
17
Index

Summary

In this chapter, we introduced context engineering and explained why it is essential for building reliable and scalable AI agents. We examined how context differs from static prompts, where it comes from, and why unmanaged context leads to problems such as performance degradation, hallucinations, and inconsistent behavior. Using Claude Code as a practical example, we explored four core context engineering strategies: writing context through persistent memory, selecting relevant context dynamically, compressing context to keep it manageable, and isolating context using specialized sub-agents. We also discussed the role of system prompts, showing how effective prompts strike a balance between being too rigid and too vague. By now, you should have an idea of how modern AI agents manage context and how both developers and users can influence their behavior. In the next chapter, we will build on this foundation and begin working with more advanced agentic workflows, using the HookHub...

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Agentic Coding with Claude Code
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