Amazon Web Services announced the Agentic Catalog Experience in Amazon Quick, addressing the challenge of connecting upstream data catalog metadata to AI-powered analytics. The article states that organizations have invested heavily in catalog platforms such as AWS Glue, Databricks Unity Catalog, Snowflake Horizon, Collibra, and dbt, where they define table descriptions, column semantics, primary and foreign key relationships, glossary terms, and metric definitions. However, a significant gap remains when enabling end users for production-ready AI and trusted dashboards. The article identifies three compounding challenges: limited discoverability due to thousands of tables, semantic fragmentation and manual recreation because rich metadata does not flow through, and time to insight stretching from hours to weeks. It notes that when upstream definitions change, manually created semantics in Quick Datasets become stale, causing semantic drift. The problem is described as the last mile: translating catalog context into a curated experience for grounded AI answers and deterministic dashboards. The announcement introduces the Agentic Catalog Experience in Amazon Quick as the solution.