Red Hat AI 3.4: Agentic AI Model-as-a-Service Platform

Red Hat is advancing its AI portfolio to bridge the gap between AI experimentation and production. The company announced significant advancements across its Red Hat AI offerings. Red Hat AI 3.4 is a comprehensive platform designed to simplify the development and deployment of agentic workflows. It aims to help organizations move beyond pilot programs to scalable AI across their infrastructure. A central feature of this release is Model-as-a-Service, or MaaS. This provides a single, governed interface for developers to access curated models. It also allows administrators to track consumption and enforce policies. The platform builds on high-performance distributed inference, powered by vLLM and llm-d. Red Hat AI also introduces new AgentOps tools. These tools manage agents from development to production with integrated tracing, observability, cryptographic identity, and lifecycle management. The platform helps resolve the tension between AI builders and infrastructure administrators. It provides transparency and control for scalable inference and autonomous agent deployments. This matters because it allows organizations to scale autonomous systems while maintaining control, security, and hardware efficiency.