Grace Ableidinger is an AI Engineer and Developer Advocate at Red Hat based in Raleigh, NC. She is passionate about inference optimization, through open source projects, like vLLM and llm-d, and finding the intersection of AI with high-impact industries. She is dedicated to building communities and resources that empower people to use AI to build a better world.
Learn how to run OpenClaw on Red Hat OpenShift with production-grade security and observability. We cover default-deny network policies for blast radius containment, container-level sandboxing with OpenShift, Kubernetes Secrets for credential management, and end-to-end OpenTelemetry tracing with MLflow, so every decision your AI agent makes is isolated, auditable, and safe by default. Whether you're a developer exploring AI agents for the first time or a platform engineer thinking about running agentic workloads at scale, this is the infrastructure story that makes it production-ready.
Learn how to build security hygiene into OpenClaw by using containers for isolation, role-based access control (RBAC) for user access permissions, and secrets for sensitive information. This article explores how to use infrastructure powered by open source technology to help protect these workflows.
Video
