New metal-to-agent capabilities in Red Hat AI provide a foundation for scaling models and autonomous agents across the hybrid cloud
Bangkok - May 28, 2026 — Red Hat, the world’s leading provider of open source solutions, today announced significant advancements across the Red Hat AI portfolio to help bridge the gap between AI experimentation and production-grade operational control. By delivering a unified, metal-to-agent platform, Red Hat AI 3.4 simplifies the development and deployment of agentic workflows, allowing organizations to move beyond pilots to scalable AI across their entire infrastructure.
By providing a consistent framework for both builders and operators, Red Hat provides a foundation for organizations to scale autonomous systems while maintaining the control, security capabilities and hardware efficiency required by the modern enterprise.
What is Red Hat AI 3.4?
Red Hat AI 3.4 is a comprehensive platform that delivers the architectural foundation and operational tools necessary to scale models and agentic workflows across the hybrid cloud. Central to this release is the delivery of Model-as-a-Service (MaaS), which provides a single, governed interface for developers to access curated models while enabling administrators to track consumption and enforce policies. This builds on a foundation of high-performance distributed inference, powered by vLLM and llm-d, to maintain optimized and efficient model serving across a wide range of environments.
While AI agents drive exponential demand for inference, Red Hat AI provides the capabilities for organizations to deploy and manage agents at scale, regardless of agent framework. Newly introduced AgentOps tools manage agents from development to production with integrated tracing, observability, cryptographic identity and lifecycle management.
To integrate enterprise data with models and agents, Red Hat AI 3.4 introduces prompt management - treating prompts as first-class data assets - and evaluation hub for assessing model and agent accuracy, quality and safety. These capabilities are powered by MLflow, which provides integrated experiment tracking and artifact management for both generative and predictive AI use cases. The platform empowers users to validate model and agent safety with automated safety testing and red-teaming for models and agents, using technology from Chatterbox Labs and the Garak project to provide a security-forward path from experimental pilots to production-ready enterprise utility.
Why does Red Hat AI 3.4 matter?
The transition from experimental chatbots to production-grade autonomous systems requires a fundamental shift in how IT teams collaborate. Many organizations now recognize the need to move from being merely “token consumers” to "token providers" to better manage costs and power private, sovereign AI use cases. However, the friction between builders and infrastructure administrators remains a primary hurdle to adoption. Without a unified approach that aligns these two roles, infrastructure access barriers slow innovation while "shadow AI" shortcuts introduce ungoverned risks and unpredictable costs.
Red Hat AI 3.4 helps resolve this tension by providing an enterprise foundation for scalable inference and autonomous agent deployments, delivering the transparency and control required to meet rigorous risk and governance standards. Because agents operate with a level of independence, the lack of visibility into their decision-making creates a critical security risk. Red Hat AI addresses this by providing the infrastructure to trace actions, reasoning steps, and tool calls, making it possible to audit how an agent arrived at an outcome. By integrating cryptographic identity management, the platform ties actions to a verified identity, helping identify which entity performed the task. Together, these capabilities move organizations beyond disconnected pilots to treat AI as a scalable, predictable, and, most importantly, accountable enterprise utility.
What Red Hat and partners are saying
“The agentic era represents an evolution of our platform from running traditional applications to powering intelligent, autonomous systems,” said Joe Fernandes, vice president and general manager, AI Business Unit, Red Hat. “We are defining the open standard for how the enterprise executes AI. By providing a hardened, metal-to-agent foundation for AI inference, MaaS and AgentOps, Red Hat provides the operational assurance organizations need to innovate at scale while maintaining rigorous control.”
“CoreWeave's collaboration with Red Hat is grounded in a shared commitment to openness and delivering a high-performance inference foundation that allows enterprises to scale their most complex AI workloads,” said Urvashi Chowdhary, Vice President of Product Management - AI Services at CoreWeave. "Together, we’ve delivered a deployment blueprint for Red Hat AI Inference on CoreWeave Kubernetes Service to run the same inference stack on-prem and in the cloud, with Kubernetes-native control and production-grade performance. This enables enterprise AI teams in regulated industries to focus on the important work: building and scaling AI, not retooling their stack for every new environment.”
"Autonomous, long running agents in the enterprise demand a new level of infrastructure control and security to ensure trustworthy operations at scale," said John Fanelli, Vice President, Enterprise Software, NVIDIA. "Red Hat AI Factory with NVIDIA provides a unified, open source-driven foundation that gives developers and operators the governance and confidence necessary for the agentic future."
Key takeaways
Deeper details
Availability
Red Hat AI 3.4 is expected to be available later this month.
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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. 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.
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