Overview

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 framework Identify which tasks are safe to automate-and which are not Eliminate hallucinations with structured outputs and grounding Build robust pipelines using RAG, validation, and tool use Engineer prompts like production systems-not experiments Optimize cost, latency, and performance across model tiers Implement 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.

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