Run Agents with Guardrails: Isolated Sandboxes Next to Your Models and GPUs

​Can you give your agents room to work without losing control?

​As agent, evaluation, and reinforcement learning workloads scale, so does the challenge of controlling what their code can access. Each task needs the environment it requires, but execution still has to stay within your platform’s policies.

​CoreWeave Sandboxes offers a different path. It gives teams isolated CPU and GPU environments for agents, evaluations, and RL workloads, with one API across CoreWeave Kubernetes Service (CKS) clusters and serverless. You can build on the infrastructure and governance controls you already use while keeping execution close to your models and GPUs.

​Join CoreWeave for a 40-minute deep dive into CoreWeave Sandboxes. You’ll learn how to set guardrails for agent requests, control network access, extend execution to serverless when CKS capacity is full, and preserve the state your agents need between runs.

​In this webinar, we’ll cover and demonstrate:

• ​What a sandbox is, and how one API places CPU and GPU sandboxes across CKS and serverless

• ​How to set guardrails once so agent requests stay within policy

• ​How opt-in spillover sends new sandboxes to serverless when your CKS clusters are full

• ​How to control network access and preserve the state your agents need between runs

• ​How to run agents, fan out evaluations, and verify RL rewards at scale

​Learn how to scale agent and evaluation workloads next to your models and GPUs while keeping execution within the boundaries your platform requires.

​Register here: https://utm.io/utdPw