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The talk documents a concrete, production-tested eval architecture that closed the loop between offline simulation and live agent behavior at scale, directly enabling Lyft's resolution rate to climb from 10% to 35%.
The talk identifies a concrete regression in evaluation rigor — from data-science-grounded practices to ad hoc LLM-graded metrics — and maps five specific failure modes that teams building on agents are repeating at scale.