CoreWeave Forge aims to speed up the AI improvement loop
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 new way of thinking about the AI loop in a way that allows you to go from first agent to your very best agent as quickly as possible,” she said. “What it does is put all these disparate pieces together so different teams can talk to each other more effectively.”
Seitinger spoke with theCUBE Research’s John Furrier and Dave Vellante at the Fully Connected event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed CoreWeave Forge, the AI improvement loop and the company’s new partner network. ( Disclosure below.)*
Building a repeatable AI improvement loop
CoreWeave launched Forge to turn each production run into a better model and agent. The loop moves through five stages: run, observe, curate, improve, evaluate and repeat. It then starts again, with each pass meant to build on the last. Speed of learning is the measure that matters most, according to Seitinger.
“The faster you learn, the faster you ship, the faster you get value. That comes back to the entire platform and the entire cloud that we’re building,” she said.
Several new capabilities target the hardest parts of that cycle. Agent Lens lets teams inspect what their agents are doing, and Registry models checkpoints and agent configurations. RL Rollouts and model distillation handle improvement, while CoreWeave’s AI Research and Iteration Agent, or ARIA, is now generally available to help users spot patterns, Seitinger explained.
“How do you take the knowledge in a large model and distill it down into something that’s specific to your use case? That’s so important for all these enterprises and organizations,” she said.
The timing matters because the workload mix is changing fast, Seitinger noted. Cognition AI Inc. is already running production workloads on CoreWeave’s first Nvidia Corp. Vera Rubin NVL72 racks.
“This idea that you’re never done, that you’re going to do post-training, that you’re going to continue to improve is a reality,” she said. “The expertise we have is [being] able to drive benefit even later on in the loop and when you’re running inference.”
The ecosystem is the other half of the plan. CoreWeave also introduced a partner network built on tested, co-engineered integrations.
“It’s really about giving folks more recipes and playbooks and use cases and solutions so they can get to value faster,” she said. “I want to help on our end, with the marketing work, provide more playbooks that folks can deploy quickly so that they don’t have to have all the same learnings all over again.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Fully Connected: ( Disclosure: TheCUBE is a paid media partner for the Fully Connected event. Neither CoreWeave, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)*
Photo: SiliconANGLE
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