Friday I posted that 94% of enterprises are confident their agents aren’t over-provisioned, while only a third have actually checked. Here’s what makes that gap dangerous: most agent failures don’t look like failures.
The scary output isn’t the one that’s obviously wrong. It’s the one that sounds reasonable, passes a quick glance, and quietly becomes the evidence for the next decision. That’s how an unverified guess turns into a “fact” nobody remembers approving.
The default I keep seeing: hand the agent a task, let it run, assume it’s right unless something looks broken. Trust granted up front, verification maybe later. Right?
Here’s how I’d approach it instead. Make the agent earn its authority. Start read-only, so it can recommend and nothing else. Then narrow the job until the answer is checkable. “Does this login need containment?” is something an analyst can verify in a minute. “Investigate our alerts” is not.
Then measure the one number that tells you whether your oversight is real: how often do your analysts override it? If the answer is basically never, nobody is actually reviewing anything.
Widen its authority only as the recommendations keep surviving real review. Read-only, then suggest, then act. Each step earned, each step reversible.
None of this is new, by the way. We treated new software as an untrusted workload for years. Assess its access, its blast radius, what breaks if it’s wrong. An agent is a workload that reasons. Same discipline, faster clock. This is how you go faster without getting burned.
So before you trust the agent, audit what it’s assuming. Then hand it the keys… one at a time.
Source: Before You Trust an AI Agent, Audit Its Assumptions
Originally posted on LinkedIn.
RationalAI helps organizations turn ideas like this into results: AI strategy and advisory services. Previous article: Microsoft’s AI Code of Conduct: Principles Aren’t the Achievement
