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Extreme Networks' Agent ONE Coworker represents a shift from generic chatbots to production-ready AI by leveraging consolidated network data and refined context layers. This move addresses critical failures in first-generation networking AI where unstructured data led to ineffective operational tools. The release signifies a maturation phase for generative AI in infrastructure, focusing on reducing resolution times and streamlining engineer onboarding rather than just offering conversational interfaces.
Surse care acoperăSiliconANGLE, Viakoo, econotimes.com, InfoQ
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Extreme Networks’ Agent ONE Coworker moves AI networking from dashboards to answers

SiliconANGLE9 min de cititArticolul și perspectiva sunt în engleză.

Operating a network has not changed in decades. Every network engineer knows the drill. An alert fires. You open a dashboard. Then another. Then you pull logs, review client history, ping the wireless team, and, hopefully, an hour later, there is enough context to start solving the problem. The industry spent a decade building better dashboards, even though operations teams needed fewer.

Operating a network has not changed in decades. Every network engineer knows the drill. An alert fires. You open a dashboard. Then another. Then you pull logs, review client history, ping the wireless team, and, hopefully, an hour later, there is enough context to start solving the problem. The industry spent a decade building better dashboards, even though operations teams needed fewer. The first wave of artificial intelligence in networking was supposed to fix that, but it largely didn’t. When I sat down with Nabil Bukhari, chief technology officer and president of AI at Extreme Networks Inc., at its chief information officer event in Seattle this week, he was refreshingly candid about why and didn’t point the finger at competitors. “The first generation of AI from multiple vendors in networking, or adjacent markets like security, was essentially a chatbot built on a lot of unconsolidated data,” Bukhari told me. “They demoed really well. Everybody showed amazing demos. But when you push them into actual production and people started using them, there were multiple…

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