AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work asks how far a coding agent can go when it controls a robot instead of a computer, and where it stops, and builds a system that keeps what it learns as code, as a coding agent does when it fixes a failing test.
Frontier models can control robots, but reasoning through every reach, grasp, and retreat makes manipulation slow and token-intensive. We revisit code as policy with a different division of labor: models build executable tools, code handles multi-phase motions, and models decide what to do next. We introduce URAI (Univ...
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