Building AX evals that actually work

Agent Experience evals often produce confident but meaningless results due to contaminated data and vague criteria. Effective evals require precise pass/fail/skip conditions, not subjective checks like "uses proper error handling." LLM judges with the same knowledge gaps as coding models will score broken code as correct without explicit, gap-filling criteria.