6Berkeley AI Research (BAIR) Blog·
BAIR perspective: data systems must be redesigned for, of, and by AI agents as inference costs approach zero
UC Berkeley EECS professor Aditya Parameswaran and collaborators publish a landscape survey and perspective on the implications of near-zero AI inference costs for data systems, arguing that agents will soon become the dominant workload. The piece identifies three research challenges: redesigning databases for agentic query patterns (including 'agentic speculation' generating thousands of SQL queries per user request), building infrastructure to manage and coordinate agent swarms over long-running tasks, and verifying data systems synthesized by agents. Concrete findings include that 80-90% of sub-queries from multi-agent text-to-SQL workloads are redundant, motivating new multi-query optimization and approximate query processing approaches. The post draws on the authors' own ongoing research directions including structured memory and agent-synthesized data systems.
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