- Chapter 1 — The Demo-to-Production Gap
- Chapter 2 — A Failure Taxonomy for Agent Systems
- Chapter 3 — Turning Vague Quality Goals into Release Gates
- Chapter 4 — RAG Retrieval, Grounding, and Citation Quality
- Chapter 5 — Tool Selection, Arguments, and State-Changing Actions
Failure-First AI Agents
A Practical Field Guide to Evaluating RAG, Tool Use, Memory, and Multi-Agent Systems
This book is 33% completeLast updated on 2026-08-02
A hands-on, failure-first guide to evaluating LLM agents, RAG, tool use, grounding, and production release gates—with executable Python examples and tests.
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About the Book
Most agent demos optimize for the happy path. Production systems fail in the gaps: unsupported claims, stale evidence, unsafe tool calls, brittle recovery, and tests that reward surface fluency instead of reliable behavior.
Failure-First AI Agents is a hands-on guide to finding those failures before users do. It turns agent quality into observable contracts, executable evaluations, and explicit release gates.
This Early Access v0.1 edition includes five complete chapters, runnable Python examples, and 27 focused tests. You will learn how to define failure taxonomies and measurable invariants; build deterministic test harnesses for nondeterministic systems; evaluate retrieval, grounding, and citation quality; test tool use, side effects, retries, and idempotency; and connect evidence to production release decisions.
The planned complete book contains 15 chapters. Early Access readers receive future versions as the manuscript expands.
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storymaker
A computer scientist and software engineer working with Python, Rust, LLM systems, and AI agents. Their work focuses on evaluation, reliability, and production engineering for tool-using models.
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