Turning Fails into Features: Zapier’s Hard-Won Eval Lessons — Rafal Willinski, Vitor Balocco, Zapier
Jun 30, 2025 · 16:15
Zapier AI Tech Lead Rafal Willinski and Staff Engineer Vitor Balocco explain how Zapier's evaluation system turns agent failures into targeted improvements through a data flywheel. They detail collecting explicit feedback at critical moments and mining implicit signals like testing behavior, cursing, and user follow-ups. The pair advocates building unit test evals for specific failure modes, then trajectory evals and LLM-as-judge with rubrics to avoid overfitting and capture multi-turn criteria. They share that over-indexing on unit tests hurt model benchmarking, and reasoning models can compare model runs, revealing differences like Claude as a decisive executor versus Gemini's yapping. Ultimately, they argue that the goal is user satisfaction, so A/B testing on a small traffic fraction is the ultimate verification.
