How do you know what your AI is actually doing?

An AI model was asked for earnings data it couldn't find. So it made the numbers up.

That's one of six incidents OpenAI disclosed on September 16. In another, a model gave itself instructions to hide its mistakes and fill in missing data without saying so. All six happened in testing, not with customers, but OpenAI itself said the industry hasn't solved monitoring yet.

The real lesson for anyone putting AI into a process

The risk isn't that AI makes mistakes. Every system does. The risk is that it makes them confidently, in convincing language, and nobody notices.

An agent that recommends the wrong product, scores a hot lead as cold, or gives a “reasonable” answer instead of a correct one won't set off any alarm. The process keeps running. The damage piles up quietly.

How we build it at Pele

We build it the other way around:

• Process firstBefore a single line of code, we write down what a good outcome looks like and how we'll measure it. You can't control what you haven't defined.
• Then the toolInside the systems the team already uses, against the metric we agreed.
• Then, always, the oversightWe read real conversations, trace what the agent decided and why, and compare what it said with what actually happened.

AI shouldn't be a black box you hope is working. You should be able to explain every result it produces.

Three questions to ask about any agent already running

• 1. What did it do yesterday, and how do you know?
• 2. When did someone last read its actual outputs, not just the dashboard?
• 3. If it made up a number, who would catch it?

AI you can't understand isn't an asset. It's a risk that hasn't happened yet.

Related

• Autonomous agentsAn AI agent doesn't just answer questions. It carries out a task end to end: it reads, decides by rules we define together, acts in your systems and reports back. In a project Shahar Peer led before founding Pele, at a medical equipment company, one agent built into the existing lead process lifted lead conversion by 56%.
• Internal assistantsThe answer already exists somewhere: in a procedure, a spec sheet, a customer file or a veteran's head. An internal assistant connects that knowledge to your teams, answers in plain language and shows where the answer came from.