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Architect Your Agent's Harness

The Architecture Techniques Behind AI Agents That Don't Hallucinate or Waste Tokens

This book is 100% completeLast updated on 2026-06-15

You have built an AI agent that works beautifully in a demo, then falls apart the moment real users arrive: it invents answers, burns through tokens, and freezes on slow tools. The problem was never your prompt. This book shows you the real fix, layer by layer, and turns "I hope my agent works" into "I can prove it does."

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About

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About the Book

An AI agent is code, not a prompt. The harness, the engineering layer around the model (tools, control loop, retrieval, memory, validation, guardrails), is what decides whether your agent hallucinates or wastes tokens. You do not fix those failures by editing the system prompt; you architect them into the harness.

This book takes one travel booking agent that demos well and breaks in production, and rebuilds its harness one technique at a time: grounded retrieval, semantic tool selection, guardrails in code, multi-agent validation, the memory pointer pattern, async handles, debounce hooks, evaluation, and observability. Every technique ships as runnable code with measured before-and-after numbers. Each chapter also shows you exactly what to tell an AI coding assistant to build that layer for you.

Author

About the Author

Elizabeth Fuentes

Elizabeth Fuentes Leone is a data scientist who builds and teaches reliable, cost-efficient AI agents. She turns frontier AI research into named, reproducible techniques and ships them as open-source code that anyone can run, study, and deploy. She publishes hands-on technical tutorials, and her work on stopping agent hallucinations and curbing wasted tokens is used by developers across the community, independent of any single platform or vendor.

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