Databricks says it solved the decades-old data pipeline problem that’s been slowing AI agents

For decades, data professionals have struggled with the challenge of managing both operational and analytical databases in a unified approach that doesn't introduce latency and performance degradation.Agents made the problem structural. A system that reasons continuously and acts on live… For decades, data professionals have struggled with the challenge of managing both operational and analytical databases in a unified approach that doesn't introduce latency and performance degradation.Agents made the problem structural. A system that reasons continuously and acts on live data cannot tolerate a pipeline between itself and the information it needs to act on.At the Data + AI Summit on Tuesday, Databricks announced two products aimed at collapsing that infrastructure. Lakehouse//RT delivers millisecond query latency directly on governed Delta and Iceberg tables, eliminating the dedicated real-time serving tier that enterprises have maintained alongside their lakehouses. LTAP, short for Lake Transactional/Analytical Processing, stores Postgres-native transactional data in Delta and Iceberg format from the point of write, removing the ETL pipelines that have connected operational and analytical systems for decades.Reynold Xin, co-founder of Databricks, described a simpler data stack as "the holy grail for agents" in a briefing with VentureBeat, arguing that as users vibe code more applications, the agents reasoning…