Articles

A customer service rep can type a discount into the system. The software will accept it. The customer would be thrilled. But the rep doesn’t do it, because they understand they’re not authorized to, even though they technically can. That gap between what someone can do and what they’re authorized to do is what keeps companies running. It’s also the thing missing from almost every AI agent deployment. This piece looks at why well-intentioned agents keep taking actions no one approved, why better permissions won’t fix it, and what AI agents are actually missing that every human employee already has.

Most writing about AI agents takes deployment as the default and asks how to do it well. The prior question, the one nobody seems to want to ask out loud, is whether the deployment should happen at all. Six conditions under which the honest answer is no, and why the cost of saying yes when the answer is no is larger than the cost of saying no when the answer might have been yes.

When a solutions architect hears “design a system that includes AI agents,” the conversation usually goes straight to infrastructure: where the models run, how context is managed, which vector database, how tools get exposed. These are real questions with real answers. But they’re not the architectural decisions that determine whether the resulting system actually works. The decisions that matter most are upstream of all of that, and they tend to get skipped because they don’t look like AI problems. They look like ordinary systems design problems, applied to a kind of user the field hasn’t quite figured out how to think about yet.