Database for AI Agents: 5 Evaluation Criteria
Databricks outlines five essential criteria for selecting databases to support AI agents in production, addressing memory, scaling, retrieval, consistency, and unified access.
AI agents require databases that can manage persistent state, concurrent writes, and retrieval across different memory types, unlike conventional applications. Traditional OLTP systems struggle with the continuous read-write cycles agents demand, leading to stale memory, conflicts, and latency. The five criteria—branch isolation, serverless scaling, hybrid search, ACID guarantees, and unified access—help teams avoid these pitfalls when moving agents from prototypes to production.
Branch isolation enables testing agents with real data without risking production changes. Zero-copy branching allows developers to create production-like environments in seconds without duplicating data, as seen in Lakebase Projects. This approach avoids the storage overhead of full copies while ensuring agents interact with current operational data during development.
Serverless scaling addresses idle compute costs by suspending resources during inactivity and resuming within milliseconds for interactive workloads. Lakebase’s scale-to-zero model for Postgres reduces cloud waste by aligning costs with actual usage, a critical factor for multi-agent systems that operate intermittently. Startup delays must remain minimal to support real-time agent interactions.
Hybrid search combines vector similarity, keyword matching, and metadata filtering in a single query to support both semantic and exact retrieval. Lakebase Search integrates these methods within Postgres tables, eliminating synchronization issues between separate systems. Its LTAP architecture ensures write performance up to five times faster than standard Postgres, keeping retrieved data current for agent reasoning cycles.