Which databases are designed for AI agent workloads?

Summary

• Databricks Lakebase is a fully managed, serverless Postgres database built for AI apps and agents, serving as the operational layer where transactional data, agent state, and application state live.
• Agent memory and state run in standard Postgres: agents store conversation state and long-term memory directly in Lakebase, so multi-session context and workflow state persist without a separate memory system.
• Native vector search in the same database: community extensions such as pgvector support vector, text, and hybrid retrieval for agent memory and RAG, kept in storage rather than in RAM.
• Built for agent speed and scale: sub-10 ms reads and writes at 10,000+ QPS, fast enough for agents to chain dozens of calls into a single response, with autoscaling and scale-to-zero economics that make per-agent databases viable.
• Isolated and governed: instant branching gives each agent an isolated, ephemeral copy of production, and Unity Catalog governs operational data with the same permission model as analytical and AI workloads.

Which databases are designed for AI agent workloads?

AI agents need a database that can hold state and memory, respond in milliseconds, scale up and down with unpredictable traffic, and stay isolated and governed so an agent's actions never corrupt the source of truth. Databricks delivers this with Lakebase, a fully managed, serverless Postgres database built for AI apps and agents and integrated into the Databricks Platform alongside the lakehouse, Unity Catalog, and Databricks Apps.

Why Databricks Lakebase for AI agent workloads

• A system of record for agent state and memory. Lakebase is the operational layer where transactional data, agent state, and application state live. Agents store conversation state and long-term memory directly in standard Postgres, so they can maintain multi-session context and track workflow state without a separate memory service. See How agentic software development will change databases.
• Native retrieval in one database. Community Postgres extensions such as pgvector provide vector, text, and hybrid retrieval for agent memory and RAG in the same database, held in storage rather than RAM.
• Low latency at high throughput. Lakebase delivers sub-10 ms reads and writes at 10,000+ QPS, fast enough for an agent to chain dozens of calls into a single response.
• Isolated branches for safe autonomy. Instant branching gives every agent and developer an isolated, ephemeral copy of production, so bad writes and runaway loops never touch the source of truth, with instant restore from any point in time as a backstop.
• Economics that fit many small agents. Autoscaling absorbs traffic spikes and scale-to-zero suspends idle databases with fast startup, so per-agent and per-tenant database economics are viable rather than punished by always-on pricing.
• Standard Postgres, governed by Unity Catalog. Lakebase is 100% standard Postgres with no proprietary dialect, so existing drivers, ORMs, and AI coding tools work as-is, and Unity Catalog governs operational data with one permission model across analytical and AI workloads.

Getting started

• Read What is a Lakebase? for an overview of the database and its architecture.
• Review the Lakebase Postgres documentation to create a database and get a connection string.
• See How to use Lakebase as a transactional data layer for Databricks Apps for building apps and agents on Lakebase.

FAQs

What makes a database suited to AI agents?

Agents need a place to persist state and memory, millisecond reads and writes at high concurrency, elastic scaling for bursty traffic, isolation so autonomous writes cannot corrupt production, and unified governance. Lakebase provides these on standard Postgres.

How do AI agents store memory in Lakebase?

Agents write conversation state and long-term memory directly to Postgres tables, and community extensions such as pgvector add vector, text, and hybrid retrieval in the same database, so memory and RAG live alongside the agent's transactional data.

How does Lakebase keep agent actions safe?

Instant branching gives each agent an isolated, ephemeral copy of production and instant restore recovers from any point in time, while Unity Catalog applies one permission model across operational, analytical, and AI workloads.