Your Database Wasn't Built for This

Databases were built for a world where humans were the primary users. That world is changing fast. As AI moves from copilots to autonomous systems, agents are starting to create environments, test queries, assist migrations, coordinate workflows, and make operational decisions at a speed and scale that humans cannot match. One useful way to think about this shift is that traditional software is crystallized intelligence: fast, reliable, and optimized for known paths. Agents are fluid intelligence: less optimized step by step, but far better at adapting to new situations and conditions. Production systems will increasingly need both, which means databases will need to serve not only human developers and operators, but also agents acting on their behalf.

This talk explores what that shift means for the data layer. As agents create more databases, generate machine scale traffic, run constant experiments, and operate continuously across systems and regions, old assumptions begin to break down. Databases must work efficiently across many scales, from tiny agent created databases to very large production systems. They must support safe sandboxes for experimentation, whether through instant cloning, isolated environments, or strong resource governance within shared systems. They must provide fine grained, on demand scoped permissions, strong governance, and full auditability for autonomous actions. And they must stay available through outages and failures, while providing the resource accounting and automatic cleanup needed for a world of temporary, agent driven infrastructure. The core argument is simple: production AI systems need more than a familiar transactional database. They need truth, governance, and control at machine scale, with the resilience to handle concurrency, growth, experimentation, and failure without losing correctness. Attendees will leave with a practical framework for thinking about database architecture in the agent era and what it takes to support agents and workflows in production.