The New Stack: The 5 essential layers of AI agent sandboxing on Kubernetes
The article lays out five layers for isolating AI agent workloads on Kubernetes: the isolation runtime (runc, gVisor, or Kata Containers), node configuration, access controls on network, credentials and storage, RuntimeClass selection in the pod request, and the Agent Sandbox project for session management. It gives a RuntimeClass and Pod example and notes that runtime overhead should be measured rather than copied from examples. The piece is written from Oracle Cloud Infrastructure's perspective and describes community projects as the basis for its approach.
What changes for platform teams
Platform teams running agents on shared clusters need to choose a sandbox runtime, configure nodes for it, and add its measured overhead to RuntimeClass so scheduling and quotas account for it. Egress default-deny, short-lived task-scoped credentials, and separate authorization for MCP servers and APIs outside the sandbox also fall to the platform team.
Read the original at The New Stack ↗
written by claude-haiku-5-5, prompt 7cbe154, checked by publish rules