Enterprise AI systems usually look amazing during demos. They can answer questions, automate tasks, generate workflows, and even act like smart assistants. But once these systems move into real production environments, many of them start having problems.nnThe issue is often not the AI model itself. The real problems come from things like changing APIs, bad data, workflow failures, memory overload, security rules, and complex enterprise systems working together.nnThis talk explains why LLM-first enterprise architectures often struggle in real-world environments. It also shows how modern AI systems are starting to behave more like distributed systems, where reliability, coordination, and control become very important.nnThe session will share real production lessons and explain why many companies are moving toward hybrid architectures, where traditional software handles validation and control while AI helps with reasoning and decision support.nnAttendees will learn practical ways to build enterprise AI systems that work better outside the demo environment.