CoreWeave Speeds AI Agent Deployment With Real-World Learning

CoreWeave launched unified agentic artificial intelligence capabilities that enable agents to learn and improve as they operate in the real world.

These capabilities eliminate the need to run lengthy offline evaluations of AI agents before releasing them to real users for inference, the company said in a Thursday (May 28) press release.

CoreWeave’s unified agentic capabilities bring together reinforcement learning, production inference, agent observability and autonomous improvement in one closed loop, according to the release.

The solution includes the company’s Serverless RL, which enables the post-training of AI models for multiturn agentic tasks without provisioning or managing infrastructure; CoreWeave Inference, which is designed to operate as a controllable, continuously running workload; W&B Weave, which serves as the observability layer for the continuous loop between production behavior and agent improvement; and W&B Skills and MCP Server, which turn general-purpose coding agents into AI researchers and agent builders, per the release.

The unified agent capabilities are meant to replace the way teams currently build AI because the process results in development cycles that can’t keep up with the pace of AI or the shipping of agents that later fail in production, Chen Goldberg, executive vice president of product and engineering at CoreWeave, said in the release.

“Enterprises that put agents in production first and let them continuously improve from real-world experience aren’t just building more reliable AI, they’re accelerating the path to superintelligence,” Goldberg said.

Phil Gurbacki, vice president of product, Weights & Biases at CoreWeave, said in a Thursday blog post that this new platform closes the gap between development and production, which has been where agent projects stall.

“Improve automatically, and build systems that compound,” Gurbacki said. “Enterprises that successfully close the loop will deliver the most reliable agents to users.”

The PYMNTS Intelligence report “Agentic AI Breaks Out of the Sandbox” found that the share of companies merely considering using agentic AI dropped considerably between August and November as more companies actively deployed the technology.

As of November, among firms in the United States with at least $1 billion in annual revenues, 11.7% said they were already using agentic AI tools and another 11.7% said they were piloting these tools. The figures were up from 1.7% in August, according to the report.