2 posts tagged with "data-quality"

An AI agent is only as reliable as the data it reasons from. Feed it nulls, duplicates, or stale data and it will produce confident, coherent, and wrong answers — often without any obvious signal that something is off. The LLM doesn't know what it doesn't know.

dbt's testing framework is the right place to enforce data quality before data reaches your agents. This post covers a layered testing strategy that catches the most common failure modes before they become AI failures.

Every data team has the same Monday morning ritual: someone checks whether last night's pipelines ran cleanly, hunts through logs for failures, and manually compiles a status update for stakeholders. It's important work — and it's entirely automatable.

A data quality reporting agent runs overnight, checks every layer of your pipeline, and delivers a clear, human-readable report before anyone opens their laptop. When something is wrong, the report explains what failed, what downstream models are affected, and what the likely cause is.