# There's no test suite for taste > Source: > Published: 2026-08-03 15:02:11+00:00 # There's no test suite for taste ### Automatic checks work when the question has a right answer. Most of a company's work doesn't. AI agents do real work now. They write code, draft the email, plan the campaign, file the research. And the moment you let them, you inherit [a question that used to belong to managers](https://www.insideainative.com/p/context-is-the-new-process): how do you know the work is any good? For code, most of the answer already exists. You run the tests and the type checker, and anything that doesn’t compile or doesn’t fit the shape you asked for gets handed back without a human ever looking. That layer is fast, cheap, and decades old, and it works because those questions have right answers a machine can verify - which is why agents slot into it so easily. None of those have a deterministic answer, and there’s no test you can write that returns true or false, because the thing you’re checking is a judgment and not a fact. The reason that layer feels solved is that it only covers the questions with a definite answer. Did the code run, does the output have the required fields, is the type correct. Those are real questions and catching them automatically is worth a lot, but they’re conceptually straightforward to answer. Answering “is this any good”, “is this on brand”, “is this the right call given everything else we’re trying to do” is much harder. None of those have a deterministic answer, and there’s no test you can write that returns true or false, because the thing you’re checking is a judgment and not a fact. The field has a name for this split. The deterministic checks are computational, and the judgment ones are inferential, which is a polite way of saying you need something with judgment to evaluate them, which until recently meant a person and now sometimes means another model asked to act as a judge. [Birgitta Böckeler](https://martinfowler.com/articles/exploring-gen-ai.html), who’s written the clearest version of this, is candid that the inferential side is the underdeveloped part, that we still have a lot to figure out about building good checks for whether the behavior is actually right, and that what we have isn’t good enough yet. That’s right, and I’d go a step further, because most of the conversation about this is happening about code, where at least some of the behavior can be pinned down by a test, and the harder version is everywhere else. The deterministic checks are computational, and the judgment ones are inferential, which is a polite way of saying you need something with judgment to evaluate them. For code, you at least get partial credit from the deterministic layer, the compiler and the test suite tell you a real slice of the truth before any judgment is involved. For a customer email, a positioning decision, a piece of writing meant to sound like your company, a call about which accounts are worth pursuing, there is no compiler. Nothing tells you it was right except taste, and taste lives in a person or, increasingly, in [your best attempt to capture what your taste is in a form something else can apply](https://www.insideainative.com/p/our-company-runs-out-of-a-folder). That second thing is hard in a way the first one never was, because you’re trying to write down something most people have never had to make explicit, the difference between on and off, good and not, us and not us. So the open ground is the inferential half, and inside that, the non-code corner is the least charted of all. The world already knows how to write deterministic checks. The unsolved problem is checking the work that doesn’t have a right answer, the work where being good is the whole point and nobody can yet hand you a green checkmark for it. I don’t have a clean answer to this and I’m a little suspicious of anyone who says they do, because if there were a test suite for taste we’d all already be running it. It’s the most interesting open problem I know of right now, and it implies an invisible line that caps what we can do with AI.