Databricks acquires Electric to bring Postgres to AI agent sandboxes
What's the deal? Databricks has acquired Electric, maker of PGlite, a lightweight version of Postgres that runs inside applications, browser tabs, user devices or AI agent sandboxes rather than on a separate server. The deal extends Databricks' Postgres offering beyond its lakehouse into the distributed environments where AI agents operate.
What's the endgame? Under the model Databricks outlined, each agent runs its own local Postgres instance through PGlite while synchronising state back to a central Lakebase Postgres system. Electric's real-time synchronisation engine keeps distributed agents aligned with a central record, so they avoid acting on stale information or duplicating work.
Why now? Companies are shifting towards agentic applications, which behave differently from traditional software. AI agents decide at runtime what data they need, update their context often and run in sandboxes where constant calls to a central cloud database can slow execution.
Databricks argues a single managed database no longer suffices, because agents need both durable, governed data and fast-changing local context.
The traction: Electric had already gained developer momentum through PGlite. Weekly downloads grew from 1 million to 13 million between August 2025 and August 2026.
Its technical roots run deep in the Postgres ecosystem: Databricks said PGlite was built on foundational WASM Postgres work by Stas KelvichDealroom has a profile for this one. Try Dealroom →, a co-founder of NeonDealroom has a profile for this one. Try Dealroom →, before Electric turned the concept into an embeddable product used in millions of projects each week.
The Postgres play: The acquisition deepens Databricks' database strategy. Lakebase, its managed Postgres offering, provides centralised infrastructure, while Electric adds a smaller-footprint implementation for edge and embedded use cases.
Databricks describes Postgres as an increasingly common foundation for AI agents, particularly where developers want one standard database model across central infrastructure and local execution.
The signal: Database and cloud providers are racing to define the infrastructure stack for AI systems. Much of that contest has focused on models, orchestration tools and vector databases, but handling state, context and synchronisation between agents is emerging as a new battleground. "The world is building a new era of agentic applications which require distributed state and real-time data synchronisation," Databricks said.
Read more: IT Brief
Image credit: Databricks
