Books — In Print
Opinionated, code-first deep-dives on the patterns that actually work in production. Each book is 4,000+ words with code samples for Claude, GPT, Gemini, and local models — plus the failure modes nobody else writes about.
Each book is one weekend of reading, one Monday of shipping.
The Shelf
The decision tree, the code, and the failure modes nobody warns you about.
Most AI agent demos fail at message four because the agent forgets the user. Five chapters covering the four axes of agent memory, the five production patterns with copy-paste-ready code, and the five failure modes nobody warns you about.
The whole system, not just the components.
Builders understand one piece — memory, or tools, or planning — but not how they fit together. This is the systems-level architecture for moving an agent from demo to production.
How agents connect to real tools, safely.
Model Context Protocol gets thrown around as an acronym. Almost nobody is implementing it cleanly. This is the implementation playbook — server design, auth, sandboxing, the patterns that scale.
The reliability discipline most teams skip.
Most agents ship without real evaluation, then break in production with no signal of why. This book is the eval framework — the metrics, the benchmarks, the test infrastructure.
Which models actually matter and how to deploy them.
Llama, Qwen, Mistral, DeepSeek, Phi — too many models, no clarity on which to use when. This book ranks them by use case, with deployment recipes for each.
The architecture call most teams get wrong.
Multi-agent is exciting and almost always premature. This book is the decision tree — when single agents work, when multi-agent earns its complexity, and the patterns that actually ship.
“If you could ask Claude this and get the same answer, the book gets rewritten until you can't.”
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