CoreWeave launches Forge to close the AI improvement loop
At its Fully Connected event, CoreWeave announced CoreWeave Forge, a platform that links deployment, observability, curation, improvement and evaluation into a repeating loop so each production run (ideally) yields a better model and agent. It includes Agent Lens for inspecting agent behaviour and a Registry for checkpoints and agent configurations, plus RL Rollouts — currently in preview — and AI Object Storage, which now supports cross-region writes. CoreWeave says the loop stays open to different models, frameworks and clouds, and launched a partner network that adds You.com as a web-search layer for agents. According to the executives interviewed, CoreWeave worked with Nvidia and You.com to post-train Nemotron 3.5 Lightning in eight hours using RL Rollouts.
If post-training and agent iteration really can be compressed to hours and kept open to multiple frameworks, competition among specialised AI clouds shifts from raw GPU capacity to tooling and loop speed.
CoreWeave targets GPU utilization in continuous AI post-training
GPU utilization during post-training depends partly on how efficiently infrastructure moves data and loads updated models. As enterprises continually refine AI agents, reducing delays between training rounds can help keep that process moving.
The need for continual improvement is pushing CoreWeave Inc. to build a full-stack AI cloud for the agent lifecycle. You.com Inc. provides web search tools for those agents, according to Saurabh Sharma (pictured, right), chief product officer of You.com.
“What we’re seeing is that the ceiling is no longer model intelligence,” Sharma said. “The models are getting more intelligent, but it’s their ability to use the tools that dictates the agent’s success.”
Sharma and Corey Sanders (left), senior vice president of product at CoreWeave, spoke with theCUBE Research’s Dave Vellante and John Furrier at the Fully Connected event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed tool skills, graphics processing unit utilization and continuous model improvement. (* Disclosure below.)
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