Databricks is acquiring Electric, a startup that brings WebAssembly-based Postgres databases into application environments, for an undisclosed sum, in an effort to provide developers a way to run data closer to AI agents as they build increasingly autonomous applications.
While traditional applications typically rely on a centralized database to handle application data, agentic applications can involve multiple agents working independently for minutes or hours, performing numerous operations, creating the need to access a centralized database repeatedly. Databricks argues that those repeated trips could add undesired latency to the application, in turn creating a case for local, isolated databases where agents can work with data directly while still synchronizing with a central database to avoid latency issues.
Electric’s PGLite and real-time data synchronization engine, Electric Sync, combined together, do just that by giving developers the option of running a local Postgres-compatible database for an agent while still synchronizing relevant data with a central database, the company wrote in a blog post.
Post the acquisition, PGLite will complement Lakebase, Databricks’ at-scale Postgres database, it added.
That combination of databases, it further added, will give developers a two-tier database architecture: PGlite can handle data locally within an application or agent environment, while Lakebase can serve as the centralized database for shared and persistent data.
Databricks also has a degree of continuity between the two technologies. PGlite builds on foundational WebAssembly Postgres work by Stas Kelvich, who co-founded Neon, the Postgres company Databricks acquired in 2025 and used as the foundation for Lakebase.
For developers, this dual-database architecture could be beneficial, analysts pointed out. “Running a database inside an agentic application or inside an agent’s sandbox can help make agents faster, especially for complex and longer-running tasks, as local access cuts down on network hops, in turn reducing wait times,” said Pareekh Jain, principal analyst at Pareekh Consulting.
“It can also make agents more reliable when connectivity is poor, though the advantage will be smaller for simple agents or tasks that require only a few database calls,” Jain added.
For CIOs, that same reduction in network hops could mean cost savings, as local execution would reduce the number of remote database calls, although the magnitude will depend on the workload and the amount of state being processed, said Chandrika Dutt, research director at Avasant. That reduction in database calls, echoed Amit Kumar Jena, AI development manager at IT consulting firm Kanerika, could also lower infrastructure costs by reducing the need to provision a fully managed database instance for each agent.
However, Manoj Chandra Jha, principal analyst at Nord-IQ Research, cautioned that the cost and reliability benefits remain unproven, as Databricks is yet to deploy the architecture at production scale and the gains will depend on data synchronization and governance holding up in real-world deployments, not just the “architecture being sound on paper.”
There are other concerns as well, especially in governance and security.
“The dual-database architecture introduces a new dimension of data governance. CIOs will need to consider what enterprise data can be materialized in an agent environment, how that data is secured and retained, how local state is audited and deleted, and how synchronization and conflicts are managed,” said Dutt.
More so because most enterprise teams haven’t dealt with this kind of data governance before, echoed Jena.
“Central warehouse governance is a solved problem. Sandbox-level state isn’t, and Databricks just moved early on it. Every CIO evaluating agent platforms should be asking vendors the same question: how does state get secured and torn down inside an agent, not just inside the central database?” Jena added.
That sandbox-level state, Jha pointed out, also adds security concerns: “Distributing state across agent sandboxes expands the attack surface and fragments governance, forcing enterprises to extend access control, audit, and compliance frameworks beyond a single database perimeter into hundreds of ephemeral local instances.”
It can also make the underlying data harder to manage, Jha added, as eventual consistency and conflicting agent actions based on stale local state could be more difficult to trace and reconcile than failures in a traditional centralized system of record.
However, that same dual-database architecture could give Databricks a leg-up against its rivals, at least for the time being, Dutt pointed out, as none of them, including Snowflake, currently offer the same WASM-Postgres capability.
“We haven’t seen a comparable move from Google Cloud or Teradata either,” echoed Jena.
But having a differentiated architecture does not necessarily translate into a lasting competitive advantage, Dutt cautioned. “Its significance will depend on how important the local state becomes in enterprise agent architectures and whether Databricks can provide the security, governance, observability, and consistency controls needed for enterprises to adopt it.”