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    GenAI Education

    Book four in the Super AI Engineering Series

    The demo worked once.

    Then real users arrived. The agent looped on a broken tool, followed instructions hidden in an email, and ran up a bill several times the forecast. This is a decision guide to agents that survive that week: 28 chapters, 148 illustrations, not a single line of code.

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    AI Agents Made Simple: A Decision Guide to Building AI Agents That Work

    A demo is one run. The product is every run after it.

    A demo answers one question: can the agent succeed? A product has to answer harder ones: how often it succeeds on requests nobody chose, what each run costs, and what the worst run can reach. An agent that gets one request in ten wrong passes a three-request demo about three times in four, then gets about a hundred wrong in its first thousand real requests. Most agent projects fail because they built the wrong thing, not because they built it badly. This book helps you decide what to build.

    Every pattern, the same four questions

    Most explanations of agents answer the first question and stop. This book gives the other three as much room, and the fourth is where it takes sides.

    How does it work?

    The mechanism, as a process you could build in any language.

    What breaks?

    The failure modes you will actually meet, each with the symptom you will see in a run and the fix.

    What does it cost?

    Latency, tokens and money, complexity and operational risk, rated in the same four-row table in every chapter.

    When is it overkill?

    A verdict with conditions you can check in your own system, never "it depends", and the simpler thing to try first.

    In your coding agent. Every chapter shows where its pattern lives inside the coding agent you already run, so you can test each claim against your own tool the day you read it.

    Built not to go stale. It names no framework, SDK, model or vendor, and quotes no token prices or benchmark scores. The next framework you meet will have parts you can already name.

    28 chapters in seven parts

    In the order decisions arrive on a real project: whether to build an agent, what it can touch, how you will know it works, and only then the more ambitious patterns.

    Part I

    Before You Build

    Whether you need an agent at all, what one actually is, and the fixed workflows to try first.

    1. 1Do You Even Need an Agent? (free)
    2. 2The Loop: What an Agent Actually Is
    3. 3Workflows First

    Part II

    Giving the Model Hands

    Tools: how calling works, how to design them, the four kinds of tool that matter most (retrieval, the web, code, and standard protocols), and skills.

    1. 4How Tool Calling Works
    2. 5Designing Tools the Model Can Use
    3. 6Retrieval as a Tool
    4. 7Acting on the Web and the Screen
    5. 8Running Code
    6. 9Plugging In: Standard Protocols for Tools and Agents
    7. 10Skills: Know-How Loaded on Demand

    Part III

    Making It Reliable

    Context, evals, tracing, guardrails, human approval, and cost. The work that decides whether an agent survives contact with real users.

    1. 11Context Engineering
    2. 12Evals: Knowing Whether It Works
    3. 13Tracing and Observability
    4. 14Guardrails and Prompt Injection
    5. 15Humans in the Loop
    6. 16What an Agent Costs

    Part IV

    Planning and Reasoning

    Interleaving thought with action, planning ahead, checking your own work, and spending more thinking where it pays.

    1. 17Reason, Act, Observe
    2. 18Plan, Then Execute
    3. 19Reflection and Self-Correction
    4. 20Search and Thinking Budgets

    Part V

    State and Memory

    Runs that outlive a crash, and what an agent should keep between sessions.

    1. 21State, Checkpoints, and Long-Running Work
    2. 22Memory, the Short Version

    Part VI

    Many Agents

    Whether to split the work, and how orchestrators, workers, and handoffs actually behave.

    1. 23Should You Go Multi-Agent?
    2. 24Orchestrator and Workers
    3. 25Handoffs and Shared State

    Part VII

    Shipping

    Deployment, the anatomy of a coding agent, and the ways agents fail.

    1. 26Deploying an Agent
    2. 27Anatomy of a Coding Agent
    3. 28How Agents Fail

    Around the parts

    Introduction and appendices

    Why agents demo well and ship badly, then one decision tree from a task to the simplest design that handles it, with a pre-launch checklist and every term defined.

    • Introduction: The Demo That Worked Once
    • Appendix A: Choosing an Architecture
    • Appendix B: Glossary

    148 illustrations. Here are four.

    Every chapter opens with a run going wrong and one analogy that carries the idea, and both are drawn. These four come from the introduction and Chapters 12, 14 and 27.

    The demo is one short, watched run on a request its builders chose, while the product is every run after it, on requests nobody chose, mostly unwatched
    Introduction: The Demo That Worked OnceThe demo is one short, watched run on a request its builders chose, while the product is every run after it, on requests nobody chose, mostly unwatched.
    A driving examiner marks each maneuver along a fixed route, so a car that arrives on time after rolling through a stop sign still fails
    Chapter 12: Evals: Knowing Whether It WorksA driving examiner marks each maneuver along a fixed route, so a car that arrives on time after rolling through a stop sign still fails.
    A teller reads everything in the envelope, forged note included, but the drawer limits what any note can cost and the vault sits out of reach
    Chapter 14: Guardrails and Prompt InjectionA teller reads everything in the envelope, forged note included, but the drawer limits what any note can cost and the vault sits out of reach.
    The harness around one model, where the loop passes through context, skills, tools, permission checks, hooks and verification, and a subagent runs a smaller copy of the same loop
    Chapter 27: Anatomy of a Coding AgentThe harness around one model, where the loop passes through context, skills, tools, permission checks, hooks and verification, and a subagent runs a smaller copy of the same loop.

    Written for the people who decide what gets built

    Developers building agents

    Mechanisms you can implement in any language, and the failure modes to look for in your own runs.

    Technical product managers

    The verdicts: what each pattern buys, what it costs and when to say no, in terms you can bring to a design review.

    Teams with an agent already running

    Start at Appendix A, a decision tree from the problem you have to the chapter that fixes it, and jump straight there.

    No machine learning background needed. You need to know that a model reads text and writes text, and to have called one from code or watched a coding agent work.

    Why this book vs. everything else

    ComparedThis BookFramework docsPapersBlog posts
    No code required
    Says when a pattern is overkillpartial
    Cost rated in every chapterpartial
    Framework-agnosticpartial
    Illustrated throughoutpartialpartial

    What you get

    • PDF and EPUB, both included, $59
    • Full-color PDF, 484 pages, with live links
    • EPUB for any e-reader
    • Instant download after checkout
    • 7-day money-back guarantee
    • 148 custom illustrations across 28 chapters
    • No code, and nothing to clone or install

    From the author of RAG Made Simple and Agent Memory Made Simple

    Nir Diamant is an AI researcher and open-source educator. His GitHub repositories have earned over 85,000 stars and draw more than 500,000 views every month. His DiamantAI Newsletter reaches 40,000+ subscribers and ranks in the top 0.1% on Substack.

    This is the fourth book in the Super AI Engineering Series. RAG Made Simple and Agent Memory Made Simple go deep on what Chapters 6 and 22 cover in one chapter each. None of them is required reading here.

    Build the agent the job needs.

    Or find out the job needed no agent at all. Get the decisions that still hold when the next framework ships.

    Get the book, $59$59 · PDF + EPUB · 7-day guarantee

    Need your agent to remember? Get it with Agent Memory Made Simple for $89 ($118 bought separately).

    Or get all four books in the series for $159 ($236 bought separately).