Every team building with AI agents eventually hits the same wall: a demo that dazzles in a sandbox and falls apart in production. The difference between a fragile prototype and a system you can actually trust isn't a smarter model — it's structure. This book hands you that structure. Across eight chapters, Jonathan Owens distills the patterns now powering production agentic systems at scale: the reasoning loops that keep agents grounded (ReAct, Reflection, Tree-of-Thought), the tool-use disciplines that make function calling and the Model Context Protocol safe at scale, the memory architectures that let agents remember what matters and forget what doesn't, the planning patterns that turn vague goals into reliable multi-step execution, and the multi-agent orchestration patterns — orchestrator-worker, debate, structured handoff — that let specialized agents collaborate without chaos. The final chapters tackle what most books skip entirely: guardrails, evaluation-driven development, and the observability, cost control, and rollback discipline that separate a side project from a system you can put your name on. Every pattern is grounded in working code, real-world case studies, and exercises built for hands-on engineers — not abstract theory. Whether you're shipping your first agent or hardening your tenth production system, this is the field guide for building agentic AI that holds up under real-world pressure. For developers, architects, and technical leaders ready to move past the demo and build something that lasts.
