How do agents work, and how should they work?

Everyone is building agents, but few people can say what actually happens between a prompt and a finished task. Under the hood, an agent is a language model running in a loop: it reads its context, decides on a step, calls a tool, reads the result and decides again. In this panel, the RAG Pack, four educators known for making hard ideas in AI clear, take the agent apart piece by piece: how the model chooses its next action, what retrieval and memory really add, and why agents that impress in a demo often stall in practice. Then they turn to the harder question: how should agents work, and when does a simple pipeline win?