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DatabricksIppokratis Pandis, Nikita Shamgunov, Reynold XinExplainer
How agentic software development will change databases
Summary
AI agents are changing software development from a slow, linear process into rapid evolutionary experimentation, creating many small, ephemeral applications and databases. The post identifies three resulting database requirements: near-zero marginal cost with elastic growth, native branching for rapid variants, and open ecosystems that agents can use reliably. Lakebase addresses branching through O(1) metadata copy-on-write at the storage layer, so agents can branch production or test state without physical data copying and pay only for experiment compute. Its decoupled, serverless compute can scale in sub-second intervals and down to zero, while standard open Postgres page formats in cloud object storage keep data accessible to external engines and tools. Lakebase telemetry reports about 10 branches per project on average, some nested beyond 500 iterations, and roughly four times as many databases created by agents as by humans.
Context
The post connects the accelerating volume and shorter lifetimes of agent-generated applications with changing database requirements. It presents Lakebase as an architecture designed for evolutionary branching, low marginal cost, seamless growth, and open data access.
Approach / What changed
Lakebase separates storage and compute, uses O(1) metadata copy-on-write at the storage layer for near-zero-cost database branches, and runs database compute elastically, including scaling to zero when idle. It stores standard open Postgres page formats directly in cloud object storage so agents, external analytical engines, and other tools can interact with the data without relying on a proprietary storage format.
Takeaways
Lakebase branches production or test database state through O(1) metadata copy-on-write, avoiding expensive physical data copies while experiments run.
About half of the agentic applications described have database compute lifetimes under 10 seconds, making fixed monthly database cost floors economically unsuitable for many workloads.
Open Postgres interfaces and storage formats give agents and external engines more direct operational and data access than proprietary alternatives.
FAQ
How does Lakebase create database branches without copying all the data?
It uses an O(1) metadata copy-on-write branching mechanism at the storage layer. Agents can branch production or test data instantly, without expensive physical copying, and pay for database compute only during the experiment.
Why does agentic development require databases that scale to zero?
Many agent-generated services are ephemeral or used only occasionally; the post says about half have database compute lifetimes shorter than 10 seconds. Scaling compute to zero when idle removes the cost floor that always-on infrastructure would impose.
Why are open Postgres storage formats useful for AI agents?
Agents are more familiar with open-source databases such as Postgres because their interfaces, behaviors, and error codes appear widely in training data. Lakebase also stores standard open Postgres page formats in cloud object storage, allowing external analytical engines and tools to interact with the data natively.
Source and freshness
- Source
- databricks.com
- Published
- Added to Yomu
- Last updated
Summaries are generated from the original by a language model. Check the original before relying on it.
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