A Future Look at Data Systems for Agents

An article from Berkeley’s EPIC Data Lab titled "Intelligence Is Free. Now What?" proposes a shift in focus from generating intelligence to managing it as inference becomes cheaper, requiring new data systems for AI agents to support long-running operations, coordination, and knowledge accumulation. Concurrently, a paper detailing Trellis, a database architecture centered around the agent experience graph, suggests treating the entire search history of an agent as a primary database abstraction rather than disposable logs. This approach, already partially implemented by Meta, aligns with Berkeley's vision by enabling crash recovery, collaboration, and continuous learning through a shared, durable data system that supports stateless agents and cross-agent coordination. Trellis redefines memory management in AI systems by promoting collective intelligence and treating search processes as database workloads, ultimately positioning itself as a response to Berkeley's research agenda for future data systems in AI.

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