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Red Hat puts safety and observability at the core of enterprise AI with Red Hat AI 3.5. Red Hat | Published 10 Sept 2026 COMPANY NEWS: Major advancements across the Red Hat AI portfolio deliver the verifiable trust, operational control, standardised architectures and performance transparency required to run AI as a shared enterprise service. Red Hat, the world's leading provider of open source solutions, 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 rigour 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. What is Red Hat AI 3.5? Red Hat AI 3.5 delivers the operational foundation organisations need to scale AI in production and extend it across hybrid environments through new safety and observability capabilities. With this release, organisations 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 utilisation, and AI model performance. Non-admin users can access dashboards for per-user token consumption showback and distributed inference workloads. In addition, 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. For organisations that require stronger isolation between tenants, Red Hat AI now officially supports running on Red Hat OpenShift hosted control planes deployed on Red Hat OpenShift Virtualisation. Hosted control planes give every tenant a dedicated cluster control plane while consolidating the hardware beneath them. Running AI workloads in Red Hat OpenShift Virtualisation virtual machines adds robust VM-level isolation across shared, GPU-enabled infrastructure. Together, these capabilities let infrastructure providers operate and upgrade the entire underlying environment from a single point of control. 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 optimises GPU spend by adjusting compute dynamically based on query complexity. Why does Red Hat AI 3.5 matter? As enterprise AI pilots succeed and initial results show promising returns, IT teams must then address the need for delivery at scale. But scaling AI across the business demands the same operational rigour as any mission-critical infrastructure: verified safety before deployment, precise resource controls across shared GPU environments, governed agent behaviour and transparent usage metrics. Most organisations today face these challenges with fragmented tooling and manual processes that don't scale. Red Hat AI 3.5 addresses this gap by unifying safety, multi-tenancy, agentic development and observability into a single enterprise platform. The result is AI that operates as an accountable, governed AI architecture - not an isolated experiment - across hybrid cloud environments. What Red Hat is saying. "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." Key takeaways. * 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 to protect real-time inference and allows background workloads to use available capacity. * 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 that intercept malicious tool calls. * Enterprise data grounding and efficient reasoning: AutoRAG with pgvector support, native AutoML, and Inference-Time Scaling (ITS) 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 utilisation dashboards for clear operational and usage transparency. * Pre-built agent templates for faster development: AI Hub introduces agent templates and starter kits with pre-configured reference implementations for common enterprise patterns, including code review, document processing, and research workflows. Deeper details. * Hosted Control Planes and OpenShift Virtualisation support for better resource utilisation with multitenancy: Red Hat AI now officially supports hosted control planes, giving each tenant a dedicated control plane and better hardware consolidation. Support for AI workloads in OpenShift Virtualisation VMs allows multiple tenants to share physical servers with strong VM-level isolation. * New batch of validated models with AI safety scores: More than 20 new validated models added to the Catalogue, including models from Google (Gemma 4), NVIDIA (Nemotron 3), Alibaba Cloud (Qwen) and more. These validated models have full performance benchmarking and now include integrated Garak safety, PII exposure, and toxicity risk scores to provide full transparency to enterprises assessing AI model risk. * Validated tool-calling models: A select group of validated models have been tagged as validated for tool-calling in the Catalogue to give customers confidence when choosing a model they wish to use for agentic use cases. * Traffic management and safe model updates: Priority-aware serving provides admission control and priority-based request routing to protect real-time inference while allowing background workloads to use available capacity and Controlled model rollout manages traffic during model updates, enabling safe transitions with minimal service disruption. * Efficient GPU memory management: the general availability of CPU offloading and developer preview of storage offloading allow models to handle longer conversations and larger documents without requiring additional GPU hardware. * Multimodal serving: Serves text, audio, and image generation within a unified serving layer via vLLM Omni (available in early access). * Multi-cloud Kubernetes serving: Extends llm-d distributed inference further beyond OpenShift onto third-party Kubernetes services, offering a consistent model serving experience across clouds. Now generally available on CoreWeave CKS and Microsoft Azure and with Amazon EKS joining as technology preview. * Agent development kits: AI hub introduces pre-configured agent templates and starter kits for common enterprise patterns such as code review, document processing, and research workflows, integrating frameworks, tools, and deployment configurations to run in sandboxed environments with operational controls and security policies intact from first deployment through production. * Enterprise data workbench: Kubeflow Spark Operator, available as a developer preview, brings distributed data processing directly into the active workbench environment, unifying data preparation and model serving on a single platform. * GPU-as-a-Service and multi-tenancy: Features namespace isolation, hosted control plane support, and real-time dashboard visibility into hardware inventories, active utilisation, and dynamic GPU capacity borrowing. Availability. Red Hat AI 3.5 is now generally available. Red Hat AI 3.5 is also now available as part of Red Hat AI Factory with NVIDIA. Connect with Red Hat 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 data centre 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. Collaborating with partners and customers, Red Hat helps them build, connect, automate, secure and manage their IT environments, supported by consulting services and award-winning training and certification offerings.

Red Hat AI 3.5 expands AI safety and observability tools. Thu, 10th Sep 2026 (Today) Red Hat has launched Red Hat AI 3.5, adding tools for AI safety, observability and multi-tenant infrastructure management. The release is Red Hat's latest effort to position its AI software as a platform for companies moving from pilot projects to broader operational use. Among the main additions is the general availability of EvalHub, designed to help organisations assess AI models before deployment. It supports safety benchmarking and auditable compliance reporting for custom models, retrieval-augmented generation systems and AI agents. Red Hat has also introduced dashboards to give platform teams a clearer view of inference health, GPU use and model performance. Non-admin users can also see token consumption showback and monitor distributed inference workloads. Safety focus The release places particular emphasis on pre-deployment testing and governance. Evaluated catalogue models now include built-in Garak benchmark scores, along with safety, personally identifiable information exposure and toxicity risk scores. More than 20 validated models have been added to the catalogue, including models from Google, Nvidia and Alibaba Cloud. A smaller set has also been marked as validated for tool-calling, relevant for agent-based applications that need models to interact with external tools and systems. Another part of the update addresses security around AI agents. Support for the Responses API and built-in retrieval-augmented generation is now generally available, while integrated NeMo Guardrails are intended to block malicious tool calls. Another new element, AutoRAG, links enterprise data repositories to agentic applications. It includes multilingual document support, conversational testing and contextual retrieval, along with a visual pipeline to help teams test configurations before deployment. Shared infrastructure Red Hat AI 3.5 also expands support for running AI workloads across shared GPU infrastructure. Fair-share GPU scheduling is intended to manage resource allocation across tenants, while priority-aware serving routes requests according to workload importance. This is intended to protect real-time inference jobs while allowing lower-priority background workloads to use spare capacity. Controlled model rollout has also been added to manage traffic during model updates and reduce service disruption. For customers seeking stronger tenant separation, the software now officially supports hosted control planes on OpenShift Virtualization. This gives each tenant a dedicated cluster control plane while allowing hardware to be consolidated underneath. Support for AI workloads inside virtual machines on OpenShift Virtualization is also intended to provide isolation at the VM level on shared GPU-enabled systems. This allows infrastructure operators to manage and upgrade the environment from a single control point. Operational visibility Observability is another core part of the release. New dashboards provide visibility into hardware inventories, active utilisation and available GPU capacity, along with model and agent performance monitoring and MLflow visual tracing for agent workflows. Red Hat is also adding per-user token metering, which could help organisations track internal usage and assign costs across departments or teams. This reflects a broader shift in enterprise AI projects as finance and IT teams seek clearer controls over spending and consumption. On the performance side, Inference-Time Scaling adjusts compute use dynamically based on query complexity. CPU offloading is now generally available, and storage offloading is being offered as a developer preview to help models handle longer conversations and larger documents without extra GPU hardware. The release also extends model serving support beyond OpenShift to third-party Kubernetes services. This is now generally available on CoreWeave CKS and Microsoft Azure, while Amazon EKS has been added as a technology preview. Agent templates To support AI application development, AI Hub now includes agent templates and starter kits for common uses such as code review, document processing and research workflows. These packages combine tools, frameworks and deployment configurations intended to run in sandboxed environments with security policies in place. Joe Fernandes, Vice President and General Manager of the AI Business Unit at Red Hat, said the market focus has changed as businesses seek to run AI systems with the same discipline expected of other critical technology operations. "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," Fernandes said. "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."