We have come a long way in this book on AI techniques, introduction to transformer that gave birth to different large language models, prompting, building agents with different design patterns through different frameworks. Now that we are comfortable building agents, in the upcoming chapters, we will get to know how to make an agentic solution deployable and reliable in a production environment. Here’s the thing that AI agents and humans have in common: if they don’t have feedback, they don’t really grow—they just repeat themselves. You can get away with that in a polished demo where the world is neat and predictable. But outside the demo? Life is messy. Customers phrase things in ways you didn’t anticipate, markets swing, and policies shift with little warning. An agent that can’t listen, adjust, and learn from those changes will eventually crack. It might not happen on day one, but the brittleness shows up when it matters most.