Red Hat today announced significant updates across the Red Hat AI portfolio with the release of Red Hat AI 3.5. As enterprise teams move past early experimentation and pilot successes, IT and platform engineering leaders face the challenge of running AI with the same operational rigor as mission-critical infrastructure. By providing the scalable foundation required to control, secure and observe these workloads across the hybrid cloud, Red Hat AI 3.5 bridges the gap between isolated AI pilots and a fully governed enterprise architecture.
Red Hat AI 3.5 delivers safety and observability at core of enterprise AI
EvalHub enables verifiable pre-deployment safety and evaluation
New observability dashboards provide real-time insight into inference health and GPU utilization
Enhanced multi-tenancy with hosted control planes and OpenShift Virtualization support
AutoRAG links enterprise data to agentic applications with multilingual support
Pre-built agent templates for code review, document processing, and research workflows
Red Hat AI 3.5 delivers the operational foundation organizations need to scale AI in production and extend it across hybrid environments through new safety and observability capabilities. With this release, organizations can verify models before deployment through EvalHub, enabling risk-focused safety benchmarking and the creation of regulatory compliance certifications. New observability dashboards give platform teams comprehensive metrics to gain real-time insight into inference health, GPU utilization, and AI model performance. Non-admin users can access dashboards for per-user token consumption showback, and distributed inference workloads.
Red Hat AI 3.5 expands on proven enterprise platform capabilities to deliver enhanced multi-tenancy for AI service providers and AI use cases that require complete hardware-to-software isolation as well as priority-aware serving with native multi-tenancy for shared GPU infrastructure. Red Hat AI now officially supports running on Red Hat OpenShift hosted control planes deployed on Red Hat OpenShift Virtualization.
Red Hat AI 3.5 also accelerates the path to governed AI agents. AutoRAG links enterprise data repositories straight to agentic applications, introducing advanced capabilities such as multilingual document support, conversational testing, and contextual retrieval. A visual pipeline gives teams confidence in their RAG configurations before deploying. Agent templates deliver pre-configured implementations for common patterns like code review, document processing, and research workflows. Once deployed, Inference-Time Scaling optimizes GPU spend by adjusting compute dynamically based on query complexity.
Verifiable pre-deployment safety and evaluation: Evaluated catalog models feature built-in Garak benchmark scores, while the general availability of EvalHub automates safety and auditable compliance reporting for custom models, RAG and agents.
Shared GPU control for multi-tenant inference: Fair-share GPU scheduling manages resource allocation across tenants, while priority-aware serving provides admission control and priority-based request routing.
Agent APIs and gateway security: General availability support for the Responses API and built-in RAG provides a unified open-source interface for multi-turn agent conversations, reinforced by integrated NeMo Guardrails.
Enterprise data grounding and efficient reasoning: AutoRAG with pgvector support, native AutoML, and Inference-Time Scaling allow models to adapt compute usage dynamically based on query difficulty.
Built-in observability and MaaS showback: Delivers per-user token metering, performance dashboards for models and agents, MLflow visual agentic tracing, and GPU utilization dashboards.
Pre-built agent templates for faster development: AI Hub introduces agent templates and starter kits with pre-configured reference implementations for common enterprise patterns.
"The conversation has moved from getting AI into production to running it at scale as trusted enterprise infrastructure, which requires safety evidence, governed agents, cost attribution and multi-tenancy," said Joe Fernandes, vice president and general manager, AI Business Unit, Red Hat. "With Red Hat AI 3.5, we are delivering the operational controls, verifiable trust and agentic foundations IT leaders need to run AI as a safe, controlled and accountable enterprise AI architecture across the hybrid cloud."
About Red Hat
Red Hat is the open hybrid cloud technology leader, delivering a trusted, consistent and comprehensive foundation for transformative IT innovation and AI applications. Its portfolio of cloud, developer, AI, Linux, automation and application platform technologies enables any application, anywhere—from the datacenter to the edge. As the world's leading provider of enterprise open source software solutions, Red Hat invests in open ecosystems and communities to solve tomorrow's IT challenges.
