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 systemsThis 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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