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CoreWeave's Forge platform addresses the critical need for continuous optimization in agentic AI workflows by integrating tools for observation, curation, and improvement. By streamlining the feedback loop from initial deployment to refined agent performance, the solution directly accelerates the iteration speed essential for production-grade AI systems. This full-stack approach effectively unifies disparate development pieces to enhance collaborative efficiency among machine learning teams.

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CoreWeave Forge aims to speed up the AI improvement loop

SiliconANGLE8 octombrie 20264 min de cititArticolul și perspectiva sunt în engleză.

Enterprises racing to put agents into production are learning that the AI improvement loop never really ends. Agents must be run, observed, evaluated and improved continuously. CoreWeave Inc. has been assembling a full-stack platform aimed at agentic AI. Its Forge offering is intended to give business units and machine learning teams a shared workflow, according […] The post CoreWeave Forge aims to speed up the AI improvement loop appeared first on SiliconANGLE.

Enterprises racing to put agents into production are learning that the AI improvement loop never really ends. Agents must be run, observed, evaluated and improved continuously. CoreWeave Inc. has been assembling a full-stack platform aimed at agentic AI. Its Forge offering is intended to give business units and machine learning teams a shared workflow, according to Susanne Seitinger (pictured), vice president of product marketing at CoreWeave. “It’s our…

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