Agentic Data Engineering: A Practitioner’s Guide

The Problem

Every data team is experimenting with AI. But ask the same question twice, and you get different answers. When the capability isn't there, the model improvises instead of stopping. And everyone starts from scratch - each engineer figures out their own prompts and workflows, the good ones die in a DM, and nothing scales.

Meanwhile, write access gets handed out like read access, and nobody has decided which AI workflows are safe to run against production.

This guide provides the framework to go from experiments to standards.