CoreWeave Closes the Training-to-Inference Gap for Autonomous Agent Improvement
With the launch of unified agentic AI capabilities, enterprises can ship agents that improve using real-world data, paving the way for the superintelligence loop
CoreWeave, Inc. (Nasdaq: CRWV), The Essential Cloud for AI™, today announced the launch of unified agentic AI capabilities that accelerate progress toward the superintelligence loop, a closed feedback loop between training and inference. 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.
Until now, training reliable AI agents meant running lengthy offline evaluations for months before releasing them to real users for inference. Not only was this process too slow, but the agents often failed because the eval datasets couldn’t cover all possible real-world scenarios. As AI accelerates the path toward superintelligence, that process is no longer viable. CoreWeave 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.
