AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Traditional data systems face profound limitations in the AI era, relying on human-crafted pipelines, lacking semantic understanding of heterogeneous data, and operating through rigid, reactive processing. To address these challenges, we propose a new paradigm called the Data Agent, designed to manage, process, and ana...
The database community is at a pivotal moment. Large Language Models (LLMs) and AI agents are rapidly changing how users interact with data systems. The traditional model—where human experts write SQL queries or navigate complex BI tools—is being disrupted by a new vision: data agents capable of understanding natural l...
DataSpace, a benchmark in which data agents produce verifiable tabular results from task-local heterogeneous workspaces, is introduced and key challenges for improving data-agent reliability are identified.
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