Arpit Bhayani

Every database architecture decision we have made relied on a few assumptions: callers are predictable, writes are deliberate, connections are short, failures are obvious, and schemas are understood. For decades, these held because a human was always in the loop. Agentic AI changes that. Autonomous, LLM-powered agents generate queries through reasoning, write at machine speed, hold connections during long chains of thought, fail quietly, and interpret schemas through a model rather than shared context. When we attach them to existing data layers, the assumptions we never formalized start to show their cracks. This talk walks through those assumptions, the production issues that follow, and how to design databases for agents. Database patterns that once seemed nice-to-have become essential once agents are in the system.