What we learned verifying AI answers across multiple models

While building Arcytic, we noticed something unexpected.

The biggest AI failures weren’t obvious hallucinations.

They were confident answers that were subtly wrong — and hard to catch.

When we compared the same question across multiple models, two patterns kept showing up:

1. Strong agreement usually meant the answer was safe to trust.

2. Disagreement was a reliable signal of risk, even when answers sounded confident.

This changed how we think about using AI for real decisions.

Curious if others here have noticed similar failure modes.