Agentic Data Systems: Why AI Agents Need More Than a Vector Database

This article discusses the shift from traditional chatbots to agentic workflows in enterprise AI, highlighting how AI agents generate complex, speculative queries that overwhelm conventional data platforms. It introduces three patterns for agentic data systems: data systems for agents (optimizing high-volume queries), data systems of agents (shared memory and state coordination), and data systems by agents (verifying agent-generated modifications). The piece argues that vector databases alone are insufficient and advocates for a broader architecture incorporating structured memory, query reuse, state management, coordination, and auditability. It emphasizes the need for an 'agentic data substrate' that treats agent activity as a first-class workload, separating concerns like querying, memory, and trace capture.