The actual search queries are far more complex than what is presented in the AI demonstrations.

​ I believe there is an overlooked issue in the design of AI products, which is that real search behaviors are actually very chaotic. In the demonstrations, what people input are clear and explicit queries. In reality, queries are everywhere: Brand names. Spelling mistake. Half-remembered product names. Local slang. Mixed language phrases. Navigation attempts. Sensitive words. Business intentions. Or, just some random fragments of what the user actually wants. Therefore, for a search or discovery system, the primary issue is not always "getting more results". The first question is to understand exactly what kind of query you are dealing with. Is the user looking for a well-known brand? Are they trying to navigate to a certain place? Do they show a purchase intention? Is this a shortage of supply issue? Is this a security or policy issue? Is this query too vague to be answered directly? For AI agents, this is particularly important because the agent may not only return a list of links but also take action based on the query. Improper query understanding may lead to incorrect recommendations, abnormal tool calls, or insecure blank states. I believe that query classification and intent splitting will become a more important part in intelligent customer service business than people expect.