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Lightning Talk - AI4NI @Runtime#

@Scale: Networking 2026 digest Program Core pick 25 August 2026

Abstract

YuLing Chen (Meta)

  • Why: Most AI-for-networking content in this program targets planning and design time; this is the runtime side — agents embedded in live operations for reliability, performance, and efficiency, with production trials behind it. That is the closest thing to deployable ML-for-networking evidence in the program.
  • What: AI4NI @Runtime: AI agents across three dimensions — reliability, performance, and efficiency — with the solution architecture, example agentic systems, initial findings from production trials, and the key technical challenges and planned directions. (from abstract only)
  • How: Agent architecture embedded in runtime operations; the mechanisms are not detailed in the abstract, but production trials are cited as the evidence base.
  • Where applicable: Meta's production network serving billions of users; early-stage, org-specific results — the value is the architecture and the honest challenge list, not yet-transferable outcomes.