Most people who work with modern AI can already get useful results from it. Far fewer can explain why a system that reads a million tokens still misses a detail, why an agent that ran for three hours fails on the fourth, or why the same model behaves differently in two products. This book is about those questions. It explains the engineering problems a frontier model must solve when it works with very large amounts of information, reasons through difficult tasks, uses tools, operates software, continues work for a long time, recovers from failure and acts within limits.
It is the advanced companion to Navigating the AI World. That book explains the foundation: what AI is, how to think about it, where it fits in work and how to learn with direction. You do not need to have read it first. If the basic ideas in Part I feel unfamiliar, it is the better place to start, and this book will point you back to it where that helps.
General AI concepts appear here only as short reminders. The purpose is to go deeper without making the language harder. Where a term is needed, it is explained in plain words first and then used consistently.
GPT-6 Astra and Claude Fable 5.1 appear throughout as real technical case studies. They are evidence for the book's argument, not its subject. The book does not try to declare a winner. It uses what each vendor has publicly documented to show how modern AI systems are actually built, and it says clearly where the documentation stops. Most of the book concerns digital work; one chapter follows the same engineering pattern out into the physical world to show what changes when an AI action has consequences beyond the screen.
Frontier-model specifications and prices change quickly. Time-sensitive values in this edition were checked against primary vendor sources on the date shown in the back matter and should be rechecked before they are relied on.