We give AI agents access to our databases, email systems, and payment APIs. And then we just... trust them.

Think about what we're actually doing. We build an AI agent. We give it tools — the ability to read and write our database, send emails on our behalf, call external APIs, sometimes process payments. We test it. It works. We ship it. And then we go home and it runs unsupervised, taking real actions in the world, with no meaningful check on what it's doing beyond "the LLM will probably stay in bounds." The LLM will not always stay in bounds. One bad prompt, one edge case, one injected instruction in data the agent reads, and it does something it shouldn't. By the time you notice, it's already happened. I'm not talking about AGI risk. I'm talking about an agent sending 500 emails to unsubscribed users, or deleting records it shouldn't, or forwarding customer data to an API it was told to use in a context it shouldn't have been. The surprising thing isn't that this happens. It's that almost nobody has a governance layer — policy enforcement, audit trail, human approval for high-risk actions — sitting between the agent and its tools. We just ship and hope. We built something to fix this, but more interested in the broader question: why is this not standard practice yet?