Amazon Quick's Agentic Catalog Experience bridges metadata gap with AI-powered discovery

Amazon Quick has announced the Agentic Catalog Experience, a new AI-powered workflow designed to connect upstream data catalogs directly to analytics end-users. The feature addresses a critical gap: while enterprises invest heavily in metadata-rich platforms like AWS Glue, Databricks Unity Catalog, Snowflake Horizon, Collibra, and dbt, that rich context rarely flows into BI tools. Curators traditionally face three compounding challenges: limited discoverability across thousands of tables, semantic fragmentation requiring manual recreation of descriptions and relationships, and weeks-long cycles that lead to stale answers and semantic drift.

The Agentic Catalog Experience solves this via the Quick Agent, which scoped to catalog discovery, creation, and inheritance. Curators describe their needs conversationally—for example, "I need tables for quarterly revenue reporting and cost analysis"—and the agent searches the entire catalog using business descriptions, tags, Gold/Silver/Bronze classifications, and quality scores. It then assesses metadata readiness and, with a single confirmation, auto-creates Catalog-Generated Datasets and Topics that inherit all upstream definitions including table descriptions, column semantics, primary/foreign keys, glossary terms, and metric definitions. This eliminates manual re-entry, ensures consistent meaning across teams, and reduces time-to-insight from weeks to hours. The result is grounded, trusted Q&A and deterministic dashboards that stay in sync with evolving upstream metadata.