Agentic AI arrives at Nutanix  

Nutanix, a hybrid multicloud computing company, has launched a full software stack called Nutanix Agentic AI. The system aims to help customers accelerate the adoption of agentic AI for business transformation. The software comprises products that are either already generally available or currently in early access and are expected to be available soon.

The shift to agentic AI has increased enterprise adoption, with challenges now centred on managing the infrastructure required to securely run large numbers of agents at scale rather than on building individual models or agents. Infrastructure and platform teams require systems that support the development and operation of AI environments, enable shared access to resources, and address performance, security and data sovereignty requirements.

At the same time, data scientists and agentic AI developers require access to tools and services for running and fine-tuning models, building agents, and securely connecting these systems to enterprise data.

“Contrary to AI infrastructure for model training that was optimised to run ‘one big job’, production Agentic AI infrastructure needs to handle scale and high rates of change for thousands of AI services, agents, and concurrent users and developers,” says Thomas Cornely, Nutanix EVP for product management.

“Nutanix Agentic AI extends our AHV hypervisor, Flow Virtual Networking, Nutanix Kubernetes Platform, and Nutanix Enterprise AI to deliver a cloud operating model to enterprise AI factories, enabling infrastructure and platform teams to simply build, operate, and govern AI factories, while providing Agentic AI developers with the performance and rich set of models and AI platform services they need.”

The software integrates with Nvidia AI Enterprise at the Agent Builder layer and orchestrates the Nvidia-certified ecosystem of AI factories for supported configurations. It enables customers with dynamic, multiuser AI environments to build, run, and protect agentic AI applications with a suite of infrastructure orchestration and security software coupled with AI Platform Services (PaaS) and models-as-a-service (MaaS) for data scientists and Agentic AI developers. Nutanix and Nvidia are working together to build a foundation for autonomous agents in the enterprise through integration with the Nvidia Agent Toolkit including the Nvidia OpenShell open-source runtime.

AI apps and factories

Nutanix says the Agentic AI software provides the following:

1. Agentic AI services and a Kubernetes platform: This AI PaaS and Kubernetes native software layer consists of:
2. An Advanced AI Gateway and Model-as-a-Service: The latest release of Nutanix Enterprise AI (NAI), version 2.6, now includes an AI Gateway service for unified policy control over cloud-hosted and private LLMs. New support for the Model Context Protocol (MCP) server and Fine Tuning extends its existing robust MaaS capabilities to enable agents to securely connect to enterprise tools and data sources. NAI now includes support for the Nvidia Nemotron family of open-source AI models, datasets, and training tools designed to help developers build agentic AI systems that can reason, securely access tools, and complete complex multistep tasks independently.
3. An open Kubernetes platform with a rich AI catalogue: Nutanix simplifies the path to Agentic AI by extending its CNCF-compliant Nutanix Kubernetes Platform with a rich catalogue of pre-built open-source AI developer tools including Notebooks, Vector Databases, MLOps workflow engines, and Agentic frameworks. Because it is fully integrated with Nvidia AI Enterprise software, developers can instantly deploy Nvidia NIM microservices, including Nemotron, to accelerate the development of high-performance AI applications in production.

2. Infrastructure optimisation and security: In the early access version of Nvidia topology aware AHV, the Nutanix AHV hypervisor has been enhanced to automatically optimise allocation of physical resources to virtual machines on GPU dense servers and help maximise performance. The Nutanix Flow Virtual Networking solution has been enhanced to offload the network dataplane to Nvidia BlueField, delivering high-performance networking while reducing host CPU and memory consumption. These enhanced capabilities bring all the benefits of virtual machines for workload and tenant isolation, day 2 operations, and infrastructure resilience to Agentic AI workloads with maximum performance, security, and resource utilisation to help achieve lower cost per token.

3. Foundational data services for AI: Agentic AI applications require foundational Data Services. As a solution built on the Nvidia AI Data Platform reference design, Nutanix Unified Storage delivers linearly scalable read/write performance for thousands of GPU clients. By providing a high-capacity tier for KV Cache offloading and support for S3 over RDMA and NFS over RDMA, Nutanix provides a scalable, low-latency data fabric that maximises GPU efficiency across all enterprise AI workloads.

According to the company, the Nutanix Agentic AI system operationalises and accelerates adoption of Agentic AI with Nvidia-certified AI factories. Customers can deploy AI factories on hardware from Cisco, Dell, and Supermicro, supported with joint validation by Nutanix and Nvidia.

Steve McDowell, NAND Research chief analyst, says: “Nutanix’s Agentic AI stack removes much of the infrastructure friction that can slow down enterprise AI projects. By bringing the layers together—from models-as-a-service at the top, to an AI platform built on a standardized Kubernetes distribution, down to GPU-aware hypervisors and DPU-accelerated networking—organisations get a more coherent AI stack, enabling AI factories that deliver strong performance and security while driving down the cost per token.”

Justin Boitano, Nvidia VP for enterprise AI platforms, says: “Agentic AI requires high-performance infrastructure that can securely manage thousands of agents at enterprise scale. Nutanix’s integration of Nvidia Agent Toolkit and open Nemotron models gives enterprises a foundation for building and operating efficient AI factories — and we’re working together to scale autonomous AI in the enterprise as AI agents continue to evolve.”