# Amazon description — AI Agents in Production: Building Reliable, Scalable, and Secure LLM Agents
# ASIN: B0GHPVL52F
# Series: The 1'st AI Engineering Series

Friday. 4:52 PM. Your agent — the one that looked brilliant in staging — sends the wrong answer to a real customer, calls the wrong tool on live data, and leaves your team reconstructing what happened from incomplete logs. This is how production agent failures actually happen: not spectacular model breakdowns, but quiet engineering failures the system was never designed to survive.

THIS BOOK IS FOR YOU IF:

• You have a working agent and need to make it production-ready
• You work with LangChain, LlamaIndex, AutoGen, CrewAI, or custom stacks
• You need tool safety, monitoring, and security — not another "getting started" tutorial
• You are responsible for agent systems where failures have real business consequences

WHAT BREAKS IN PRODUCTION — AND HOW THIS BOOK FIXES IT

• Tool misuse → Chapter 3–4: safety contracts, input validation, write-access controls
• Planning loops that never terminate → Chapter 5–6: bounded planners, loop guards, step limits
• State corruption across sessions → Chapter 9: state design patterns, consistency guarantees
• Zero observability when things break → Chapter 11: structured logging, session replay, alerting
• Prompt injection and data leakage → Chapter 13: security hardening, access control, adversarial defense

THE PRODUCTION READINESS LADDER

The core framework of this book: a clear path from Demo-Capable → Assisted → Guarded → Trusted → Scaled. Most teams fail not because they lack capability, but because they jump from Level 1 to Level 5 without earning each level of operational maturity.

17 CHAPTERS + 2 APPENDICES — Design Review Checklists · Release Approval Checklists · Reusable Templates

Compatible with OpenAI, Anthropic, and open-source models. Works with LangChain, LlamaIndex, AutoGen, and CrewAI.

The teams mastering production agent engineering in 2026 will define what reliable AI means in their organizations. The teams skipping it will spend Q3 fixing what they shipped in Q1.