CoreWeave Wants Enterprises to Ship Agents First and Fix Them Later

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Building reliable AI agents has traditionally meant doing most of the hard work before anyone uses them. Developers run lengthy offline evaluations against labeled datasets, measure performance across quality, accuracy, cost, and style benchmarks, make improvements, and repeat the cycle until the numbers look acceptable. Only then does the agent get deployed to users.

CoreWeave thinks the sequencing is wrong. Labeled datasets can’t cover every real-world scenario, and real users reliably find the gaps. The result: agents that perform well in testing, but disappoint in the wild. The GPU cloud provider’s new agentic AI platform flips the model: deploy agents to users immediately, then let real-world usage generate the signals that drive improvement.

The platform combines CoreWeave’s serverless reinforcement learning and production inference with two products from Weights & Biases, the AI development tool provider it acquired in 2025: W&B Weave for observability and W&B Skills for autonomous improvement. Together, they form what CoreWeave calls the Superintelligence Loop, a closed feedback cycle between training and inference that helps agents compound their reliability over time.

In practice, agents are deployed immediately, bypassing lengthy offline evaluation cycles. W&B Weave tracks production behavior by capturing and classifying user interactions and surfacing failure modes. Those signals feed into CoreWeave’s Serverless RL, which post-trains the model on real-world data. CoreWeave boasts that its backend has been proven to reduce costs by up to 40 percent and accelerate training by approximately 1.4 times, with no loss in quality.

In the final step of the cycle, the improved agent returns to production before the process repeats.

While it may seem unorthodox to deploy agents without extensive prior training, the approach has precedent. Recommendation systems from Netflix and Spotify, for instance, have long operated on a similar principle, launching with baseline models and continuously improving based on real-world usage rather than waiting for perfect pre-trained accuracy.

The critical difference with AI agents is what happens after deployment. Without reinforcement learning driving continuous improvement, shipping early just means failing in production. RL has historically been out of reach for most enterprise teams as it’s too GPU-intensive and operationally complex. CoreWeave’s platform puts Serverless RL at the center of the loop, making that continuous improvement mechanism accessible to enterprises that couldn’t previously run it.

“The gap between development and production has always been where agent projects stall,” Phil Gurbacki, Weights & Biases’ vice president of product, wrote in a blog post. CoreWeave’s platform is designed to close that gap — using real-world production data to continuously improve agent reliability rather than relying solely on pre-deployment evaluation cycles.