[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_29":-5,"_39":-5,"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5,"_12":-5},"book",{"_13":14,"_15":16,"_17":18,"_19":20,"_21":22,"_23":24},"currentUser","formToken","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvODUwMjQ","title","AI Agents, Engineered","state","published","contactAuthorMethod","email","aboutText","

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:

Foundations: what an agent actually is, and how the language model underneath works.
Building agents: the loop, planning and self-correction, tools, skills, and protocols.
Context engineering: managing the context window, retrieval, and memory across long runs.
Patterns and choices: workflows, multi-agent systems, oversight and autonomy, the outer loop, and when not to build an agent.
Making agents reliable: observability, evaluation, security and guardrails, durable state.
Shipping and operating: cost, latency, deployment, and scale.
Applications: coding agents, research and business agents, agent UX, recipes that cut across industries, and agents as classifiers and scorers.

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.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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