CoreWeave (CRWV) said it has launched unified agentic AI capabilities that accelerate progress toward the superintelligence loop, a closed feedback loop between training and inference.

AI inference is a process where a trained AI model uses its learned knowledge to make predictions or conclusions on new, unseen data.

The AI infrastructure solutions provider said that with reinforcement learning, production inference, agent observability, and autonomous improvement working as one closed loop, agents not only become more reliable, they compound in capability over time.

The company said that until now, training reliable AI agents meant running lengthy offline evaluations for months before releasing them to real users for inference. This process was slow, and the agents often failed because the eval datasets couldn’t cover all possible real-world scenarios.

CoreWeave said it eliminates this bottleneck, enabling enterprises to close the loop between training and inference. Now agents learn and improve as they operate in the real world, the company added.

“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,” said Chen Goldberg, executive vice president of Product and Engineering at CoreWeave

CoreWeave said it integrates four capabilities into a single closed loop: the company’s Serverless Reinforcement Learning, or RL, enables enterprises to post-train large language models for reliability on multi-turn agentic tasks without provisioning or managing infrastructure; CoreWeave Inference is designed to operate as a controllable, continuously running workload; visibility across every agent at scale; and Weights and Biases, or W&B, Skills and MCP server turn general-purpose coding agents into AI researchers and agent builders that work around the clock to help create reliable agents autonomously.