Libro: Building Agentic AI Systems with Claude Code: A Practical Guide to MCP, Sub-Agents, Hooks, Computer Use, and Production-Ready AI Workflows in 2026

AI can do more than answer questions. It can read your codebase, plan multi-file changes, run your tests, fix its own mistakes, and ship working software. That shift from conversation to action is what agentic AI means in practice. This book shows you how to build, deploy, and operate agentic systems using Claude Code, the most capable agentic coding tool available today.
This is not a theoretical overview or a collection of prompts. It is a field guide for builders. You will learn how the agentic loop works under the hood, how to write CLAUDE.md files that dramatically improve output quality, how to connect Claude Code to any external system through the Model Context Protocol (MCP), how to coordinate multiple agents using sub-agents and parallel execution, and how to package repeatable workflows with skills, hooks, and plugins.
Part I covers the foundations: what agentic AI actually is, how the 2026 technology stack fits together, how to get started with Claude Code, and how the agentic loop of read, plan, execute, and evaluate drives every session.
Part II dives deep into core workflows: code generation and large-scale refactoring (including real migration case studies), MCP server configuration and custom server development, sub-agent coordination patterns, skill and hook design, and browser-based computer use for end-to-end testing and verification.
Part III tackles production systems: architecture patterns for multi-agent deployments (supervisor-worker, pipeline, swarm, evaluator-optimizer), building custom agents with the Agent SDK, memory and context management for large codebases, safety and governance frameworks, and testing and evaluation harnesses that measure real reliability.
Part IV covers real-world applications: adoption patterns for software teams, practical workflows for non-engineers who want to build with Claude Code, enterprise governance and cost management, and a clear-eyed look at what comes next.
The book includes four appendices: a glossary of agentic AI terminology, evaluation rubrics for scoring agent reliability, workflow templates for common tasks, and a prompt library with ready-to-use patterns for code generation, review, refactoring, and documentation.
Whether you are a software engineer looking to 10x your development workflow, a technical leader evaluating agentic AI for your organization, or a non-engineer discovering that you can build real software by describing what you want, this book gives you the mental models, practical skills, and production discipline to do it right.

18 chapters. Built for builders who ship.