An Implementation Plan for Evaluating AI Agents on Databricks
Evaluating AI agents requires systematically checking whether the agent is accurate, helpful, safe, relevant, and efficient enough for real business use, before and after it goes live. Databricks provides this natively through managed MLflow. This post is a follow-up to my previous blog: “How Do You Know Your AI Agent is Working”.
Why a dedicated evaluation process is needed: AI agents produce varied, non-exact answers, and traditional software tests that rely on exact matches are therefore inadequate. What is needed is an evaluation-driven loop:
- Define what “good” looks like for your business.
- Test against a representative set of examples.
- Score the results automatically wherever possible.
- Bring in human experts for calibration.
- Improve the agent, re-test, and then monitor in production.
Working through this loop reduces risk, builds business trust in the agent, and makes continuous improvement practical. Databricks supports offline evaluation during development as well as online...
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