Nutanix has announced a new software solution designed to help organizations deploy agentic AI at scale. With Nutanix Agentic AI, the company is targeting enterprises that want to go beyond traditional chatbots and integrate AI into more complex business processes.
This is reported by SiliconANGLE. Many organizations use chatbots to support employees and customers via text or voice. These systems typically answer individual questions and perform simple tasks. According to Nutanix, however, the focus is shifting toward AI agents that independently navigate multiple steps, make complex decisions, and perform long-running tasks with minimal human intervention.
This development places different demands on the infrastructure. While AI systems for model training often focus on a single large computational task, Nutanix notes that agentic AI requires an environment capable of supporting thousands of AI services and concurrent users. Furthermore, the infrastructure must be flexible enough to continuously accommodate new agents, applications, and developers.
Integration with Nvidia for AI Agents
Nutanix Agentic AI is designed as part of a broader platform for AI workloads in enterprise environments. The software works with Nvidia technology to build and manage AI agents. This should make it easier for companies setting up AI factories for large-scale AI development to roll out and scale new services.
The solution builds on existing Nutanix components, including the AHV hypervisor. This virtualization layer separates software from physical hardware, enabling virtual machines to run in an isolated, cost-efficient manner. Organizations can also use Flow Virtual Networking and Kubernetes to develop, orchestrate, and manage AI agents within a cloud-like operational model.
On the infrastructure side, Nutanix has expanded the platform with BlueField data processing units for high-performance network processing. These processors are designed to handle network traffic more efficiently while reducing the load on hosts’ CPUs and memory.
The software also integrates with Kubernetes. This allows organizations to offer AI services via a platform-as-a-service model and deliver models as separate services. According to Nutanix, this helps companies better predict costs when deploying large numbers of AI agents.
Lower costs per token
Tokens are a key cost factor in AI applications. Tokens form the basis for how AI models process information and perform reasoning. The more tokens a system uses, the higher the required computing power—and thus the costs. Nutanix states that more efficient virtual machines and better resource optimization should lower the price per token.
In addition to infrastructure, the platform also focuses on developers. Nutanix has expanded its Kubernetes environment with a catalog of integrated tools. Developers gain access to notebooks, vector databases, MLOps workflow engines, and frameworks for agentic AI, among other things. Combined with Nvidia software, teams can build, test, and refine AI agents in sandbox environments before they go into production.
According to Nutanix, this approach should make it easier to securely develop and manage complex AI agents. Agentic AI operates iteratively, progressing through multiple steps to complete tasks. As a result, reasoning processes can quickly become complex. With comprehensive testing and development environments, the company aims to prevent systems from exhibiting unpredictable behavior when deployed in production.