Evaluation-First AI Agents: How Zepto Scales Customer Support on Databricks and MLflow
Summary
Zepto built its customer support agents on Databricks and MLflow using an evaluation-first approach, treating traces, golden datasets, and LLM-as-judge checks as core infrastructure and connecting development and production through a dual-loop architecture with a strict quality gate. The result: 65% lower support costs and payback in under a month, giving other AI builders a reusable blueprint for engineering reliability, cost, and risk into production-grade agents.
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