Rethinking operational databases for the agent era

For years we treated operational and analytical data as two separate worlds, and agents are exposing how costly that split is. One world ran the business; the other fed BI and, later, AI. Moving data between them added pipelines, latency, duplicated governance and plenty of drag.

Bringing cloud-native Postgres into the lakehouse, as Databricks did with Lakebase, points toward a single, scalable source of truth. Operational records and analytical history sit under the same governance model instead of being reconciled after the fact.

Agents make this more urgent than BI ever did. A dashboard can tolerate data that is a day old. An agent approving a request or answering a customer needs current operational state and historical context in the same moment, with the same access rules applied to both. Every extra copy of the data is another place where permissions can drift. It is still early, and I would not move core transactional systems on the strength of a launch announcement. But the direction is right: faster delivery, better decisions and simpler governance from fewer copies of the truth.

The fewer copies of your data an agent has to reconcile, the safer it is to let it act.