Large language models are no longer experimental. They are infrastructure.

From clinical decision systems to financial analysis pipelines, LLMs are transforming how intelligent systems are built. But most implementations fail-not because the models are weak, but because the systems around them are poorly designed.

This book changes that.

Agents in the Wild is a practitioner's guide to building reliable, production-grade AI systems using large language models. Designed for engineers, developers, and researchers, it goes beyond theory and shows you how to design, evaluate, and deploy real-world automation systems that actually work.

Inside, you'll learn how to: Design LLM-powered agents using the OODA loop frameworkIdentify which tasks are safe to automate-and which are notEliminate hallucinations with structured outputs and groundingBuild robust pipelines using RAG, validation, and tool useEngineer prompts like production systems-not experimentsOptimize cost, latency, and performance across model tiersImplement real-world architectures used in enterprise AI systems

This is not a beginner's guide. It is a field manual for professionals building AI systems in high-stakes environments.

If you want to move beyond demos and build systems that scale-this book gives you the blueprint.