HPE Teams With NVIDIA to Advance Secure Agentic AI Across Enterprises and Industrial Environments

HPE will support NVIDIA designs for securing autonomous AI agents and deploying AI factories in modular containers.

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HPE AI Factory with NVIDIA brings together accelerated infrastructure, governed AI services, and secure operations to help customers deploy and scale agentic AI across hybrid environments

BERLIN--(BUSINESS WIRE)-- NVIDIA GTC Berlin 2026-- HPE (NYSE: HPE) today announced new innovations across the HPE AI Factory with NVIDIA portfolio that help enterprises securely deploy and operate increasingly autonomous AI across hybrid and sovereign environments. HPE AI Factory with NVIDIA brings together models, infrastructure, data, and workflows to help enterprises run AI with greater control over performance and data access.

“AI agents are becoming an essential part of how enterprises operate and create value,” said Cheri Williams, SVP & GM, Private Cloud & Flex Solutions, HPE. “As organizations deploy AI across industries and increasingly sensitive environments, they need infrastructure that gives them greater control over security, sovereignty, and how enterprise data is accessed and used. We’re helping customers scale AI across hybrid environments while maintaining the governance and operational control required to move from experimentation to production.”

HPE Private Cloud AI expands secure access to enterprise AI services and agentic capabilities

For enterprises seeking to securely develop and scale AI applications, HPE is expanding HPE Private Cloud AI, the turnkey AI factory solution within the HPE AI Factory with NVIDIA portfolio, with new HPE AI Essentials capabilities that make it easier for developers, independent software vendors (ISVs), and AI agents to securely access, integrate and consume enterprise AI services. Through production-grade APIs and MCP servers, organizations can connect applications and autonomous agents to inference, model, and knowledge services while maintaining centralized governance, identity management, and operational control.

New capabilities include:

• Governed APIs for enterprise AI: Developers and ISVs can programmatically and securely consume inference, models, knowledge services, and AI pipelines to integrate HPE Private Cloud AI into applications and industry solutions.
• First-class MCP services for agentic AI: MCP servers are versioned and governed by role, tenant, and project, with auditing designed to enable AI agents to interact with enterprise systems while maintaining organizational governance.
• OpenAPI-based development: OpenAPI serves as the source of truth for supported interfaces and enables automatically generated documentation, software development kits and code samples that help partners build integrations faster.
• Enterprise-grade identity and governance: Role-based access control, machine identity support, and comprehensive audit logging are designed to help organizations manage human and autonomous access to AI services.
• Support for regulated and sovereign environments: API-first and Kubernetes-assisted onboarding are now supported across both connected and air-gapped HPE Private Cloud AI deployments.

HPE is also expanding GPU flexibility within HPE Private Cloud AI to help enterprises efficiently support diverse AI workloads. Now generally available, HPE Private Cloud AI can combine NVIDIA H200 GPUs and NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs within the same cluster, enabling organizations to support a broader mix of AI workloads through a common, governed operating environment. NVIDIA’s open-source KAI Scheduler provides quota- and priority-based scheduling to improve GPU utilization and workload placement across projects.

Schneider Electric advances agentic industrial AI with HPE Private Cloud AI

Schneider Electric plans to combine HPE Private Cloud AI, co-engineered with NVIDIA, with its industrial and operational technology expertise to support Industrial AI Agents at the Edge, bringing intelligence closer to where operational decisions are made while maintaining control over sensitive data.

HPE and NVIDIA deliver purpose-built infrastructure for deploying and securing AI factories and agents

As organizations scale AI beyond individual applications, they require infrastructure that can efficiently support autonomous agents, secure execution environments and increasingly complex computing demands. HPE is expanding its AI infrastructure portfolio with NVIDIA technologies designed to help organizations build and deploy AI factories at scale.

New infrastructure innovations include:

• Dedicated compute for agentic AI: HPE is introducing the NVIDIA Vera CPU Rack by HPE, a high-density, liquid-cooled system for CPU-intensive agentic AI and reinforcement learning (RL) workloads. With 256 NVIDIA Vera CPUs, support for 22.5K sandboxes, and 80% faster agentic CPU performance, it helps AI factories scale agent sandboxes, RL evaluations, and data-processing pipelines alongside GPU compute.
• Secure execution for autonomous agents: HPE will support the NVIDIA OpenShell Reference Architecture for Safe AI Agents, an open, full-stack architecture for building, containing, observing and securing AI agents across AI factories, enterprise infrastructure, and the edge. The architecture combines the NVIDIA OpenShell secure runtime on NVIDIA Vera, NVIDIA Sentry with NVIDIA BlueField-4 for hardware-based security enforcement, and NVIDIA Vera Rubin NVL72 for model inference, helping enterprises govern agent activity, isolate workloads and enforce security policies as agentic AI deployments scale. HPE and NVIDIA are collaborating to extend security and protection from the agent runtime to the underlying AI infrastructure throughout the HPE AI Factory with NVIDIA portfolio.
• Simplified AI factory deployment: HPE will also support NVIDIA DSX POD, a reference design for pre-fabricated and validated infrastructure in modular containers to reduce onsite integration work and shorten deployment timelines for large-scale AI factory environments. HPE will work with NVIDIA to deliver DSX POD AI factories, building upon the modular data center expertise and services offered today with AI Mod Pod.

HPE strengthen security and operations for sovereign AI infrastructure with NVIDIA

Organizations deploying AI in sovereign and regulated environments need infrastructure that protects sensitive data and models while maintaining control over security, performance and operations.

HPE is expanding support for NVIDIA technologies that address these requirements across AI factory environments:

• Confidential computing for sensitive AI workloads: To help address security and sovereignty requirements, HPE is expanding support for NVIDIA Confidential Computing on HPE ProLiant Compute DL380a Gen12 servers equipped with NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, helping organizations secure frontier AI models and protect sensitive data on sovereign infrastructure while workloads are in use. On NVIDIA-Certified Servers, confidential computing establishes hardware-based trusted execution environments designed to isolate AI workloads, including virtual machines, containers, and bare metal deployments, from underlying infrastructure.

Availability

• Updates to HPE Private Cloud AI with the HPE AI Essentials API platform, including administrator-enabled developer and ISV access, are rolling out in late 2026.
• Included in HPE Private Cloud AI is a self-service HPE AI Essentials developer experience for application and agent registration, API and MCP discovery, and credential management. This will be rolling out in 2027.
• Heterogeneous GPU support for NVIDIA H200 and NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs within HPE Private Cloud AI is generally available now.
• KAI Scheduler capabilities for HPE Private Cloud AI are generally available now.

About HPE

HPE (NYSE: HPE) is a leader in essential enterprise technology, bringing together the power of AI, cloud, and networking to help organizations achieve more. As pioneers of possibility, our innovation and expertise advance the way people live and work. We empower our customers across industries to optimize operational performance, transform data into foresight, and maximize their impact. Unlock your boldest ambitions with HPE. Discover more at www.hpe.com.

View source version on businesswire.com: https://www.businesswire.com/news/home/20261021721099/en/

Media Contact:
Kelsey Akerson
kelsey.akerson@hpe.com

Source: Hewlett Packard Enterprise

Key Terms

openapi technical

OpenAPI is a standardized, machine-readable blueprint for how a company’s web interfaces (APIs) work, like a recipe card that tells developers exactly what ingredients to send and what results to expect. Investors care because a clear OpenAPI makes it quicker and cheaper for partners, customers and internal teams to connect and build on a company’s services, which can speed product adoption, lower technical risk and uncover new revenue or partnership opportunities.

reinforcement learning (rl) technical

Reinforcement learning (RL) is a form of machine learning where a computer program learns to make decisions by trying actions and receiving feedback as rewards or penalties, gradually favoring choices that lead to better outcomes—think of training a pet with treats for good behavior. Investors care because RL can power automated trading, pricing, supply-chain or risk systems that change revenue, costs and competitive edge, while also adding model-driven risks that affect valuation.

role-based access control technical

Role-based access control is a system that gives people access to company systems, files or tools based on the job they do — like issuing keys that only open the rooms needed for each role. For investors, it matters because it reduces the chance of data breaches, fraud, or accidental errors, supports regulatory compliance, and helps preserve business continuity and trust, all of which can affect a company’s financial risk and reputation.

confidential computing technical

Confidential computing is a technology that keeps data secure while it is being processed or analyzed, even from the systems that run the calculations. Think of it like a locked box where sensitive information is kept safe inside, no matter what happens during the work. This helps protect private data from unauthorized access, making it especially important for businesses and investors concerned about data privacy and security.

trusted execution environments technical

A trusted execution environment (TEE) is a protected, isolated area inside a computer chip that runs code and stores data separately from the main system so sensitive operations remain secure even if the rest of the device is compromised—think of a locked safe inside a factory floor. Investors care because TEEs reduce the risk of data breaches, enable secure cloud and financial services, help meet regulations, and can be a competitive advantage or a cost factor for companies handling sensitive information.