Books

Build AI agents that wake up, detect problems, and take action without waiting for a prompt. Through illustrated architectures, six practical use cases, and a runnable Python project, learn how heartbeat systems turn autonomous behavior into reliable automation and how to build a business around it.

As AI models become increasingly accessible, the real challenge lies in making them work reliably in production. Advanced Harness Design explores the engineering behind robust AI systems, from architecture and orchestration to security and operations. Discover the patterns, tools and practical techniques needed to build dependable AI systems that perform in the real world.

What really makes an AI agent work? It is not just the model, but the system around it. This book takes you inside the architecture, algorithms and engineering behind AI agents, showing you how to build a complete agent harness from scratch in Python and turn it into a working system you can debug, deploy and extend.

Ready to move beyond AI prototypes? Learn how to build agents that reason, plan, use tools and tackle real-world tasks. From core concepts to production-ready architectures, this practical guide covers the code, design decisions and engineering challenges you need to build reliable, secure and scalable agentic systems.

You have been using AI as a faster keyboard.The engineers who will define the next decade are using it as a cognitive workforce they direct, constrain, and govern. The gap between those two practices is not a matter of better prompts. It is a matter of an entirely different mental model.This book is that mental model. Built from first principles. Illustrated through 28 chapters of real architectural decisions, real failures, and real production systems.From execution to orchestration. The complete practitioner guide.

Build reliable LLM applications from first prompt to production agent. AI Engineering Visualized uses memorable doodles, practical Python, RAG, evals, tool calling and safety patterns to help you ship AI systems that work on Monday.

It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.

What happens when AI starts making the decisions behind your database queries? This practical guide explores how modern optimizers work, then shows how machine learning and reinforcement learning can improve them. Learn to build, test and deploy AI-driven optimization systems that deliver real performance gains.

Static code analysis is more than running a linter and fixing warnings. This book shows how to build practical analysis pipelines with Claude Code and deterministic tools, combining AI-driven insights with reliable checks to improve code quality and security across projects of any size.

MCP can transform the way you work with Claude Code. This practical guide takes you from the basics to production, showing you how to connect tools, build integrations, automate workflows and ship reliable, secure solutions you can use right away.

Generative AI can write code fast, but fast does not always mean safe. This practical guide shows software teams how to verify, test, secure and govern AI-assisted code across the full development lifecycle. Packed with real-world examples and practical workflows, it offers a clear path to adopting AI without lowering the bar for software quality.

AI coding agents can move fast, but speed without clear intent creates expensive mistakes. This practical guide shows how Behavior-Driven Development turns specifications into a reliable contract between you and autonomous coding systems, helping agents build the right thing, catch problems earlier and produce software you can trust.

AI agents don’t fail because they forget everything. They fail because they remember badly. This book shows you how to engineer memory that stays accurate, efficient and useful over time. From SQLite and PostgreSQL to vector indexes and multi-agent systems, you’ll learn what it takes to build agents that can run for days without losing the plot.

This book is a practical guide to building and running local AI systems in 2026. Learn how to choose hardware, run modern LLMs, build RAG pipelines and AI agents, and deploy secure, efficient infrastructure while keeping full control of your models and data.