Imagine walking into a shoe store and telling the salesperson you want "sports shoes." Where do they go from there? They'll probably ask questions: "Running shoes or basketball shoes? For outdoors or indoors? What's your budget?" This intuitive back-and-forth is what's missing from most online shopping experiences. A new research paper introduces "ProductAgent," an AI sales assistant that aims to bridge this gap by asking clarifying questions. It works like this: you tell it the general product you're looking for, say “running shoes,” and the AI responds with relevant options and some pointed questions: "What's your preferred terrain? Road, trail, or track?" or "What's your price range?" As you answer, ProductAgent refines its search, just like a human salesperson would. This conversational approach makes online shopping more personalized, helping you find the perfect product even when you don't know exactly what you want initially. The researchers also built a testing system called "PROCLARE" to evaluate ProductAgent's performance. This involves using another AI to play the role of a customer, having realistic (albeit simulated) conversations with ProductAgent. The results show that this conversational method improves search accuracy dramatically as the conversation progresses. The more questions ProductAgent asks, the closer it gets to finding the "perfect" product. However, the research isn't without its challenges. Like many AI systems, ProductAgent sometimes generates irrelevant or nonsensical questions, hindering its ability to effectively pinpoint a customer's needs. Improving this question-asking logic is crucial for the future success of AI sales assistants. The potential is clear, though: imagine having a virtual shopping assistant that truly understands what you're looking for, even when you struggle to articulate it yourself. This research moves us one step closer to that reality.