Explore how Red Hat AI simplifies agent deployment with OpenClaw, showcasing model inference, safety guardrails, agent identity, and persistent state. Learn about vLLM, Llama Stack, and Models-as-a-Service (MaaS) options, and discover the benefits of agent identity and zero trust with Kagenti and AuthBridge.

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Red Hat AI provides a platform-level solution for deploying AI agents, handling model serving, safety guardrails, agent identity, and persistent state. Using OpenClaw (an open source personal AI assistant) as a reference deployment, the post covers three inference paths: vLLM for self-hosted model serving via KServe, Llama Stack for unified multi-backend inference routing, and Models-as-a-Service (MaaS) for managed serving with built-in API key management and rate limiting. Agent identity is addressed through Kagenti, which introduces AgentRuntime CRDs for lifecycle visibility and AuthBridge for zero-trust service-to-service authentication using SPIFFE/SPIRE and short-lived JWTs. OpenShift enforces security defaults including non-root containers, built-in OAuth, and automatic TLS. A community installer (openclaw-installer) automates the full deployment in about two minutes.

Deploying agents with Red Hat AI: The curious case of OpenClaw

Model connectivity: Three paths to inference

Platform security: What OpenShift enforces by default

Take the next step with OpenClaw and Red Hat AI