CoreWeave Links Agent Training and Inference in One Production Loop

From offline evaluation to a closed loop

According to CoreWeave, AI agents were until recently built by running lengthy offline evaluations against labeled datasets, making changes and repeating the cycle until metrics such as quality, accuracy, cost and style fell within acceptable ranges. Only then did agents ship to production. The company argues this approach is slow and can lead to poor reliability or even failure, because labeled datasets cannot practically cover every real-world scenario.

CoreWeave calls its answer the superintelligence loop, a closed feedback loop between training and inference. It combines four elements: serverless reinforcement learning (RL), production inference at scale, fleet-wide observability for agents and infrastructure, and autonomous improvement that uses real-world data.

Serverless RL and production inference

Reinforcement learning lets agents learn by interacting with an environment and receiving rewards or penalties. CoreWeave says RL has been out of reach for most enterprises because it is advanced and GPU-intensive. Its Serverless RL service is meant to post-train LLMs for reliability on multi-turn agentic tasks without teams having to provision and manage infrastructure.

• The service scales elastically, up when needed and down to zero when idle.
• CoreWeave manages and maintains the infrastructure and keeps jobs resilient.
• According to the company, the backend packs jobs to maximize utilization, reducing costs by up to 40% and accelerating training by approximately 1.4x with no loss in quality.
• Rollouts run on a shared GPU cluster with per-token billing.

Training and inference run on separate always-on CoreWeave instances, so edits to rollout or training loops apply in seconds rather than minutes, the company says.

On the inference side, CoreWeave Inference supports the production execution layer. Teams can choose between Serverless Inference, Dedicated Inference and Inference on CKS. Built-in monitoring surfaces performance, scaling behavior and system health so teams can detect issues early and maintain production SLOs at scale.

Observability and autonomous improvement

W&B Weave handles observability for production agents. It offers out-of-the-box signals to surface failure modes and an evaluation framework meant to prevent regressions. Built-in and custom signals capture and classify user interactions, and alerts can go through Slack notifications and trigger webhook automations. Weave organizes multi-agent traces into sessions and turns, which CoreWeave says helps pinpoint root causes of failures that would otherwise stay buried in raw logs.

For autonomous improvement, W&B Skills and an MCP server are designed to turn general-purpose coding agents into AI researchers and agent builders. Skills give a coding agent fluency in W&B tools for experiment tracking, model management, tracing, evaluations and monitoring. The MCP server provides tools and resources to access data and run experiments with Weights & Biases. CoreWeave says users can let an agent run thousands of experiments around the clock and reach production-grade reliability weeks sooner.

What it means for teams building agents

The announcement targets the gap between development and production, which CoreWeave describes as the place where agent projects stall. By bundling RL training, inference and monitoring on one platform, the company aims to let enterprises launch agents earlier and improve them with live data. The source does not give pricing or availability details, and the performance figures are CoreWeave's own claims. The company concludes that enterprises that successfully close the loop will deliver the most reliable agents to users.