After a few months, the market-research "agent" I built is really an ai report generator with a human still holding the pen

I run a small market-research shop, six of us. Earlier this year I got tired of the grind of pulling competitor pages, review data, and pricing into a client deck, so I built what I kept calling an agent. Feed it a company and a question, it goes and gathers, clusters the findings, and drafts the report. It's genuinely good at the assembly. It reads faster than any of us, it doesn't get bored on page forty of a forum thread, and the first draft lands structured instead of a pile of notes. On that part alone it saves me most of a day per project. Here's the reality check though. The thing it cannot do is decide what actually matters to this specific client. Two competitors raised prices last quarter. The agent reports both as equal signals. My analyst knows one of them is irrelevant because that competitor sells to a segment our client abandoned two years ago. The model has no way to know that, and when it guesses it guesses confidently, which is worse than leaving it blank. So it settled into being an ai report generator that does the boring 70 percent, and a person still does the judgment 30 percent that clients are actually paying for. I stopped trying to close that gap. I think a lot of us build an agent hoping it removes the human, when the honest win is that it removes the drudgery around the human. For those of you running agents on knowledge work, where's your line? What do you let it decide versus just assemble?