Every AI agent system eventually fails. Not because the model isn't capable but because the data reaching it is wrong, stale, or not structured for reasoning.

Beneath the Agents is the engineering book the AI agent revolution needed but hasn't had. While most resources focus on choosing the right model or writing better prompts, this book goes underneath into the knowledge graphs, retrieval systems, MCP protocols, memory layers, and data pipelines that actually determine whether your agent works reliably in production.

Written from hard-won production experience, this book walks engineers through every layer of the stack beneath the language model:

The four-zone context window architecture
Model Context Protocol (MCP) design and deployment
Knowledge graphs as agent long-term memory
Hybrid retrieval: dense + sparse + graph
Continuous ingestion and freshness engineering
Multi-agent architecture and observability

Across 26 chapters, you'll move from first principles to production-scale architecture learning not just what to build, but when each approach applies and when it doesn't. Practical checklists, failure-mode diagnostics, and a complete glossary make this as useful on your desk during a 2 AM incident as it is on a first read.

If you've shipped an agent that looked great in testing and then quietly started hallucinating, contradicting itself, or serving stale answers in production this book explains why, and shows you exactly how to fix it.

"The language model is the lens. What you see through it depends entirely on what you place before it."