What happens to a database when the user is an AI agent

The New Stack

by Max Liu

February 25, 2026

AI-Generated Deep Dive Summary

The rise of AI agents as primary users of databases is reshaping how these systems are designed and operated. Traditionally, databases were built to serve human users, emphasizing stability, longevity, and ease of use for tasks like query building and capacity planning. However, AI agents operate at a much faster pace, generating intense spikes in activity before discarding their workspace once their task is complete. This shift requires databases to adapt to a more dynamic, ephemeral nature. AI-driven workloads demand instant responsiveness and scalability. Agents do not slow down or optimize unless forced; they thrive on rapid, combinatorial processing. Traditional scaling methods, such as replicating entire clusters, become impractical for these high-speed, short-lived interactions. Instead, databases must prioritize elastic scaling—adjusting compute resources up or down based on real-time workload demands while minimizing storage costs. Architectures that separate compute from storage are proving essential for AI-friendly databases. Cloud object storage, for example, allows cold data to remain stored cheaply while frequently accessed data is cached near compute for low-latency performance. This approach supports rapid scaling during peak activity and efficient downsizing afterward, aligning with the transient nature of AI workloads. For DevOps professionals, this shift matters because it drives a fundamental rethinking of database architecture and management practices. Organizations must adopt systems that can handle the unpredictable, high-speed demands of AI agents without compromising performance or incurring excessive costs. The move toward more scalable, elastic, and cost-efficient database solutions is not just a trend—it’s becoming a necessity for staying competitive in an AI-driven world.

Verticals

devopscloud

Originally published on The New Stack on 2/25/2026