AI Agent Architecture - Designing, building, and deploying multi-agent systems with tool use, memory, and robust error handling. #1007415

AI Agent Architecture is a practical engineering guide to building autonomous agents that work outside the notebook. It starts from a simple observation: the gap between a demo agent and a production agent is not the model, it is everything around the model — the tools, the memory, the planning loop, the error handling, and the observability that makes all of it debuggable.

The book walks through the full production stack — agent architecture patterns, tool use and function calling contracts, short-term and long-term memory, planning and reasoning loops, multi-agent coordination, the ReAct pattern and its variants, error recovery, observability, evaluation, security, deployment, and human-in-the-loop approval flows.

It covers the failure modes that destroy production agents: a prompt injection hidden in a retrieved document that hijacks the plan, a memory that grows without bound until the context window overflows, a multi-agent handoff that loses critical state, a retry loop that burns tokens without making progress, an evaluation that rewards shortcut solutions. Each is presented with the failure, the countermeasure, and the operational tradeoff.

Fourteen chapters. Real Python code. No toy examples. Written for engineers who need agents to work at scale, not in a demo.