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Evaluation-First AI Agents: How Zepto Scales Customer Support on Databricks and MLflow

Evaluation-first AI agents at Zepto: how Databricks + MLflow power reliable, high-ROI agent systems—and a practical blueprint AI builders can apply. Skip to main content Summary How Zepto builds evaluation-first AI agents on Databricks and MLflow, using traces, golden datasets, and LLM-as-judge evaluations as core infrastructure for LLM systems. How this framework achieves 65% lower support costs, and sub-one-month payback, offering AI builders a reusable blueprint for high-impact, production-grade agent design.

Agent or tool
Databricks
Topic
Customer Support
Reported on
Case study (Databricks)
Added
2026-11-11

Why it matters

Support is repetitive and time-sensitive. Agents can draft answers, look things up and handle simple requests, so people can focus on the tricky ones. It is one of 554 sightings filed under Customer Support on Agent Sightings (number 272 by interest), and one of 37 involving Databricks.

How you could try it

Here is a reusable prompt, written by us in our own words, that captures the task. Paste it into Databricks or any similar agent and adjust the details to your situation.

I want an agent to handle routine customer questions end to end, and pass anything unclear to a person. The tool in the source case was Databricks.

Saved

Tip: Start small: run it once on a low-stakes example, read what the agent did, and only then let it run on real work.

Source

Read the original first-hand account on Case study (Databricks): https://databricks.com/blog/evaluation-first-ai-agents-how-zepto-scales-custome…. Our summary is a paraphrase; details are in the source.

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