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Designing, Building, Evaluating, and Operating Agents You Can Verify
You watched a language model do half an hour of work on its own. This book is about the question that came next: how much of it can you trust?
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About the Book
You hand a language model a task that would take a person half an hour. Instead of an answer, you watch it work: it searches, reads, tries something, hits an error, corrects itself, and comes back finished. The first feeling is delight. The second arrives a beat later and lasts longer: I didn't see every step. How much of this can I trust?
That second feeling is the subject of this book.
AI Agents, Engineered is written for the practicing engineer (comfortable with software, not necessarily with machine learning) who is evaluating agents or has just started building them, and wants to get past both the hype and the dread to the engineering underneath. Its thesis is simple: an agent is only as trustworthy as the signal you can use to verify it.
Across seven parts it covers:
Plus a minimal agent annotated line by line, a glossary, and an annotated reading map.
Two promises shape every chapter. The book is time-agnostic: no claim depends on which model is best this quarter or what a token costs today. And it is solution-agnostic: it teaches the patterns that transfer across every vendor and framework, not the syntax of one product. Prompt engineering became context engineering, then harness and loop engineering, and the names will keep changing; this book teaches what stays true underneath every renaming. Over 130 original diagrams and illustrations make the ideas concrete.
About the Author
Enrique Gutiérrez is a professor of Computer Science at the Universidad Politécnica de Madrid (UPM) and a research affiliate at the Massachusetts Institute of Technology (MIT). He works on natural language processing, algorithm design, and AI agents. His recent work turns large language models into dependable instruments: agents that classify and score real-world language, including clinical text, using explicit rubrics, carefully chosen examples, and outputs that can be checked rather than merely trusted. That habit of asking what signal verifies this system before granting it autonomy is the thread that runs through this book. He writes for engineers who want to build with agents without either the hype or the dread.
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