You got the demo working. Now it has to work on Monday. AI Engineering Visualized is for developers who have to ship features that call a language model. A model is one box; a product is the whole diagram around it. This book draws that diagram, box by box, as hand-drawn doodles you can remember and reuse when you design or debug. Every idea is a named mental model, such as The Context Suitcase, The Citation Gate, The Golden Set or The Trojan Ticket, with a picture, a plain definition, a failure you can trigger and the fix. One assistant, built chapter by chapter. You build a support assistant for Northwind Goods, a fictional online store. It starts as one prompt answering "Can I get a refund for order #1234?" and grows into a system that:

• decides refunds with rules in code the model cannot argue away
• counts its tokens and checks its own output against a contract
• calls tools, then searches a handbook and answers with citations
• says "I don't know" when the pages don't cover the question
• measures itself with evals before every change
• runs an agent loop with step limits, permissions and human approval
• retries failed calls, masks personal data and logs every request for replay

Real code, no API key. Every chapter has short Python examples from the free companion repository (MIT licence, Python standard library only) and an exercise with a tested solution. The examples run on a scripted stand-in model, so they cost nothing; swap in your provider with one function when you are ready. Who it's for: backend and full-stack developers, data engineers asked to "add AI", and technical founders. You need to read Python. You do not need a machine learning background. Who it's not for: readers who want to train models from scratch, or a catalogue of today's model prices and rankings. Prices and model names change too fast; the book shows you how to measure instead. Complete edition: all 24 chapters. All 24 chapters of Volume 1 are in this edition, fully illustrated.

Your agent worked in the demo. Then it refunded the wrong order, waited forever on a courier, or told a customer "your refund has been processed" when no payment ever went through.

AI Engineering Visualized, Volume 2: Agents is about everything around the model that turns its proposals into safe, finished work. The book calls that the agent harness: the loop, the tool registry, the checks, the state store, the approvals, the memory and the limits. The model is a part you can swap. The harness is what you test, version and ship.

One assistant, carried forward. Volume 1 ended with a support assistant for Northwind Goods, a fictional online store. Volume 2 picks it up and makes it an agent you could trust with real tickets: refunds, damaged items, late deliveries, address changes. Each chapter builds one part of the harness, shows a failure you can trigger, and fixes it.

Visual first. Every idea is a named mental model, such as The Agent Harness, The Identity Chain, Blast Radius, One Operation, One Receipt, The Waiting Ticket or Every Stop Has a Reason, drawn as a doodle with a plain definition next to it.

Real code. Each chapter has short Python examples ("Hands on Keys") and runs on a scripted stand-in model by default, so it costs nothing. Chapters also report what happened when the same code ran against a real local model (llama3.2 through Ollama), including the bugs those runs exposed and how the harness was changed to catch them.

Chapters 1-3 are in the free sample.

Who it's for

Developers who have a working LLM feature or a basic agent loop and now need it to behave on real tasks: backend and full-stack engineers, data engineers, and technical founders. You need to read Python. It helps to have read Volume 1 (especially its agent chapters), but the ideas it relies on are recapped where they are used.

Early access

This book is in early access: all 17 chapters are written, with final polish still landing. Buy once and get every revision free.