From prompt engineering to agent experience

Not long ago, the craft of working with large language models was all about the prompt. You'd carefully compose a single, curated invocation, pre-filling it with everything the model could possibly need: data from databases, context retrieved through RAG, instructions refined through dozens of iterations. We called it prompt engineering, and it felt like a real discipline. In a way, it was. But the models were already trained to follow instructions as part of a conversation. The prompt was never