Agentic AI Takes the RAN — Multi-Scale O-RAN Framework Closes the Control Loop
A new O-RAN paper formalizes the multi-scale agentic AI architecture that puts LLMs at the intent layer, small language models at millisecond control, and foundation models at the air interface — and the pattern applies well beyond six G. Plus Cloudflare closes a BGP hijack vector RPKI cannot touch, and NVIDIA's co-packaged optics switch ships to production.
Welcome to Amaze Networks for Thursday, June fourth. Quick question for you: if you needed an AI agent to control a network in real time — not in seconds, not in milliseconds, but in microseconds — what model class would you even use? We have a paper today that answers that question, and the architecture it describes goes well beyond six G.
Can I jump in before we even get to the paper? Because I think the framing of "real-time AI network control" makes people assume this is speculative research. It isn't. The problem it solves — multiple AI models competing for control of the same infrastructure at different speeds — is a problem people are hitting in production today.
Set that up for me. Where are people hitting it right now?
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