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This article focuses on the architectural shift toward "always-on" AI agents that integrate inference, feedback, and training into a continuous loop, fundamentally changing infrastructure requirements for scale and reliability. The discussion highlights critical challenges in distributed training across global data centers, emphasizing the need for near-perfect uptime and the adaptation of model architectures to emerging hardware like Nvidia's Vera Rubin platform.

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Always-on AI agents turn infrastructure into a continuous learning loop

SiliconANGLE1 octombrie 20264 min de cititArticolul și perspectiva sunt în engleză.

AI agent infrastructure is evolving to support systems that move continuously among inference, feedback and training. Cognition AI Inc.’s Devin now assists throughout the software development lifecycle, from planning and writing code to reviewing it and responding to production problems. That expanding role requires AI agent infrastructure capable of supporting continual learning at scale, according […] The post Always-on AI agents turn infrastructure into a continuous learning loop appeared first on SiliconANGLE.

AI agent infrastructure is evolving to support systems that move continuously among inference, feedback and training. Cognition AI Inc.’s Devin now assists throughout the software development lifecycle, from planning and writing code to reviewing it and responding to production problems. That expanding role requires AI agent infrastructure capable of supporting continual learning at scale, according to Silas Alberti (pictured, right), head of research and founding team at Cognition. “Our runs are…

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