A lot of “AI agent” use cases are just automation with extra steps

If a workflow can be written as “when X happens, check Y, then do Z,” an AI agent probably shouldn't be making those decisions. The interesting part is when the input stops being predictable. Give an agent a messy customer email and it can figure out what the person is asking for. That's genuinely useful. But understanding the request and deciding what happens next are two different things. Let the model figure out that someone wants a refund. Let boring, predictable software handle the actual transaction. And if getting it wrong is expensive enough, put a human in the loop. Feels like we're trying to make agents autonomous when the better use case is often just letting them handle the messy part. What are people actually letting agents do fully autonomously in production?