Nutanix today added an ability to securely run artificial intelligence (AI) agents to its existing platform for deploying inference workloads in on-premises or self-managed cloud computing environments.
Announced at the NVIDIA GTC 2026 conference, Nutanix also revealed it is working with NVIDIA to integrate with the NVIDIA Agent Toolkit, which provides access to an open source runtime, dubbed NVIDIA OpenShell, for securely deploying AI agents.
The Nutanix Agentic AI offering is an extension of the Nutanix Enterprise AI platform that makes it possible to securely orchestrate AI agents that are running on either an isolated virtual machine or container deployed on a Kubernetes cluster.
At the core of Nutanix Enterprise AI is an instance of NVIDIA AI Enterprise that enables organizations to build and orchestrate AI agents using a rich catalog of pre-built open source AI developer tools including notebooks, vector databases, machine learning operations (MLOps) workflow engines, and agentic frameworks. Nutanix has then extended the NVIDIA AI Enterprise to create a full stack platform that is integrated with its AHV hypervisor, Flow Virtual Networking software and the Nutanix Kubernetes Platform.
The Nutanix AHV hypervisor has been enhanced to automatically optimize allocation of physical resources to virtual machines on GPU-dense servers, while Nutanix Flow Virtual Networking can now offload the network dataplane processing to NVIDIA BlueField processors. The latest Nutanix Enterprise AI also now includes an AI Gateway service for unified policy control over cloud-hosted and private LLMs and support for the NVIDIA Nemotron family of open-source AI models, datasets, and training tools. Finally, NVIDIA has added support for Model Context Protocol (MCP) servers to the platform as well.
The overall goal is to provide IT teams with an integrated infrastructure that addresses everything from the underlying networking and server infrastructure to the tools needed to orchestrate workloads, says Debojyoti Dutta, chief AI officer for Nutanix.
Additionally, the Nutanix Enterprise AI platform provides IT teams with more control over costs, he adds. Rather than deploying every inference workload in the cloud, IT teams can reduce the total cost of AI by opting to deploy agentic AI applications in an on-premises IT environment that provides access to data in an IT environment that has a fixed amount of infrastructure resources. That approach is especially useful for deploying long running AI agents, notes Dutta. “The cost of running long running AI agents can quickly rocket,” says Dutta.
It’s not clear how many AI inference workloads are being deployed on some type of on-premises IT environment versus the cloud. A recent Futurum Group survey finds cloud deployments account for 35% of implementations, compared to on-premise and private cloud deployments that have a 24% share and hybrid IT environments that have a 21% share.
Regardless of how AI agents are deployed, the one thing that is certain is there will soon be thousands of them accessing data across the enterprise. The challenge and the opportunity now is determining how best to securely enable them all to be deployed without breaking the IT budget.
