Database scalability is evolving beyond traditional measures of capacity and throughput. The emergence of AI agents changes the scalability landscape, shifting focus from simply handling more data or queries to managing the creation and maintenance of numerous databases simultaneously. Multitenancy is being replaced by hyper-tenancy, which allows for much tighter isolation and greater elasticity in scaling. This transformation is driven by the need for rapid provisioning and strict data isolation, especially as applications increasingly rely on ephemeral agents that execute specific tasks for brief periods. Historical examples illustrate how technological shifts can redefine scalability. The rise of virtualization and cloud computing transformed how we view server instances, leading to a demand for faster boot times in operating systems. Similarly, the miniaturization of transistors became a priority with the advent of integrated circuits. Now, databases face similar pressures due to the proliferation of AI agents. Each agent requires its own data context and must operate independently, leading to the potential need for trillions of database instances across various applications. SQLite stands out as a fitting solution for this new scalability requirement. With trillions of installations worldwide, it excels in environments where multiple instances need to be spawned quickly and efficiently. As organizations move toward deploying countless agents to serve millions of users, the architecture of databases will need to adapt significantly, allowing them to handle unprecedented levels of demand and complexity.