AWS's new agentic catalog feature in Amazon Quick reads your existing data catalog and builds BI datasets and joins itself, collapsing weeks of manual setup into a guided conversation that takes minutes.
Picture the last time someone on your team needed a new dashboard for a question nobody had asked before. First they hunted through the catalog for the right tables. Then they had to relitigate what "revenue" means at your company this quarter, gross or net, booked or collected, because whoever built the last dashboard never wrote that down anywhere the new person could find it. Then they rebuilt all of that by hand inside whatever BI tool your company runs, because the descriptions your data team already wrote in Glue or Databricks don't travel with the data. AWS's own framing of this loop: it routinely stretches from hours to weeks, and it repeats every single time someone new needs a new view into the same numbers.
AWS just shipped a fix for the part of that loop that actually burns the time. The Agentic Catalog Experience in Amazon Quick, announced this week, is an agent scoped specifically to catalog work. It reads the metadata your data team already curated in AWS Glue Data Catalog or Databricks Unity Catalog and does the last mile itself.
You tell it what you're building in plain English, "I'm a senior analyst on Finance, I need tables for quarterly revenue reporting," and the agent searches the whole catalog using the business descriptions, glossary terms, and quality scores your team already wrote. It comes back with the tables that match.
Here's the part that actually saves the weeks. Once you pick the tables, the agent builds the datasets and works out the joins itself. It detects primary and foreign key relationships and assembles star or snowflake schema groupings automatically, the same modeling work a BI engineer would otherwise spend days doing by hand. Every dataset it creates gets tagged as inheriting its semantics, stays read-only, and queries the source catalog directly instead of copying data anywhere. If the upstream definition of "active customer" changes next quarter, you hit a sync button instead of hunting down every dashboard that baked in the old one. AWS puts it plainly: weeks of manual configuration collapse into minutes of guided conversation.
Why should a marketing or ops leader care about a data catalog feature? Because the real cost here was never the software. It was the queue. Every new question a business user wanted answered had to go through a data team translating catalog metadata into BI metadata by hand, one table at a time, and that team is a bottleneck whether you have five analysts or fifty. Collapse that translation step and your revenue ops director stops waiting behind marketing's dashboard request. The definitions stay consistent across both teams too, since everyone is inheriting from the same upstream source instead of retyping it themselves.
The pricing lands where you'd expect from an AWS product built for teams that already run enterprise data infrastructure. Amazon Quick runs $20 a seat monthly on the Professional plan or $40 on Enterprise, per Quick's pricing page, plus a flat $250 per account infrastructure fee and metered agent hours beyond the monthly allowance at $3 an hour. Not a cheap add-on. Still a rounding error against what a data team burns re-keying the same table descriptions every time a new BI project kicks off.
Here's the honest caveat. This only works if your organization already did the upstream investment. AWS built it to read Glue and Databricks Unity Catalog, and name-checks Snowflake Horizon, Collibra, and dbt as platforms enterprises use for the same curation work, but none of those connect yet. Thin or undocumented catalog? The agent has nothing rich to inherit, and you're back to typing it all in by hand. The sync is manual too. AWS says scheduled automatic sync is "on the roadmap," which is the polite way of saying that if nobody remembers to click refresh, your dashboards can quietly drift out of date with the very catalog that's supposed to keep them honest.
Metadata management has always been the unglamorous tax on every AI rollout, the one that gets ignored right up until the AI hands someone a confidently wrong number in a board meeting. Amazon Quick just made the case that the tax collector should be an agent, not a person with a spreadsheet and a Tuesday afternoon to lose.