Data Systems

Enterprise AI usually begins with a deceptively simple request: ask the system a business question and get an answer. Then reality enters, politely carrying a knife. The relevant data is not in one table. The schema is incomplete. The user’s intent depends on personal preference. A term such as “Bay Area” needs external knowledge. A PDF, a web page, an image, and a database record all matter. Someone wants the answer explained, filtered, joined, visualized, and revised after a follow-up question. The demo looked like a chatbot; the production requirement looks suspiciously like distributed systems engineering. ...

A data analyst asks a database a question. An AI agent interrogates it. That distinction sounds theatrical until the query logs arrive. The human analyst usually knows roughly where to look, asks a small number of targeted questions, waits for answers, adjusts, and eventually presents a result. The agent is less graceful. It checks schemas, samples columns, guesses joins, inspects distinct values, tries partial SQL, abandons it, starts again, validates, retries, and occasionally recruits more agents to repeat the exercise in parallel. It is not being stupid. It is compensating for a missing sense of the underlying data. ...