Tagged: evaluation

I read Dario Amodei’s essay We Must Pace the Frontier, and one part stayed with me: AI capability is moving faster than our ability to evaluate and control it properly.

This is not only a frontier model problem. We are already seeing it while building production AI systems.

In one of our workflows, the error rate was around 18%. Initially, it looked like the model was making poor decisions. After reviewing the traces, we found that the reasoning was mostly correct. The model was simply receiving the wrong context from the retrieval layer. Once we fixed that and improved how the evidence was selected and checked, the error rate dropped to 4%.

That experience changed how I think about AI reliability. Looking only at the final answer is not enough. We need to know what information the system used, why it selected that information, which actions it took, and when it decided not to involve a person.

This becomes even more important when agents are allowed to update records, communicate with customers, process financial information, or trigger business workflows.

I agree with the idea of pacing, but I think it also applies at the application level. Teams should not increase an agent’s autonomy just because a newer model performs better in a benchmark or demo. Autonomy should increase only when the system can consistently explain and support its decisions, detect failures, and escalate uncertain cases.

A confident answer and a well-supported answer are not the same thing. Production systems need to know the difference. The evidence I ask a team for before an agent acts without approval is the subject of Refuse Over Guess.

First posted on LinkedIn.