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Virtana today announced AI Factory Observability for Nutanix Agentic AI environments, extending system-aware observability across Nutanix Cloud Infrastructure and Nutanix Enterprise AI. As enterprises adopt agentic AI, the operational challenge shifts from building models and deploying individual agents to running infrastructure that can scale reliably under dynamic, high-concurrency demand. To address this, Virtana is expanding AI Factory Observability from Nutanix Cloud Infrastructure into Nutanix Enterprise AI, extending visibility and control from the infrastructure layer into AI platforms and model-driven workloads.
Virtana extends AI Factory Observability to Nutanix Agentic AI environments, covering Nutanix Cloud Infrastructure and Nutanix Enterprise AI.
The integration provides infrastructure and platform teams with shared operational visibility for managing agentic AI in production.
75% of enterprises report double-digit AI job failure rates, with over half citing infrastructure bottlenecks.
Virtana delivers real-time GPU telemetry including utilization, memory, power draw, temperature, and health.
Token-level visibility enables teams to understand throughput, latency, and resource demand under concurrency.
The solution will be demonstrated at Nutanix .NEXT 2026 in the AI Pavilion.
"As enterprises adopt the Nutanix Agentic AI platform to build and run intelligent, distributed AI systems, understanding how those workloads behave across infrastructure and services becomes critical," said Luke Congdon, VP of Product Management at Nutanix. "Virtana's extension of observability into Nutanix Enterprise AI helps provide that visibility, enabling organizations to operate AI factories with greater performance, efficiency, and control."
Virtana's AI Is Breaking Human-Managed Operations research highlights the scale of this shift, with 75% of enterprises reporting double-digit AI job failure rates and more than half citing infrastructure bottlenecks that reduce throughput, increase cost per token, and limit the ability to scale concurrent agent workloads.
"Enterprises have proven they can stand up AI infrastructure," said Paul Appleby, CEO of Virtana. "The challenge now is operating agentic AI environments where systems reason, adapt, and act across distributed resources. These are dynamic systems that demand full-stack visibility and control to optimize GPU utilization, manage cost efficiency, and support thousands of concurrent agents with the performance and governance required for production at scale."
Nutanix Agentic AI establishes a broader platform for building and operating AI factories. Within that architecture, Nutanix Enterprise AI is the layer where models, agent services, and enterprise AI workflows are deployed, connected, and scaled. As these environments move into production, understanding how AI services behave across the full stack becomes the defining operational challenge, spanning inference performance, GPU consumption, infrastructure contention, and system reliability.
"AI workloads are no longer static. They are increasingly agentic, continuously adapting how they consume infrastructure," said Amitkumar Rathi, Chief Product Officer at Virtana. "By extending AI Factory Observability into Nutanix Enterprise AI, we give organizations end-to-end visibility and control across the layer where AI services are built and operated, while connecting that activity back to the infrastructure supporting it. Platform teams can manage performance, reliability, and cost with greater precision, and data teams gain the operational context required to run AI in production with confidence."
Virtana AI Factory Observability spans Nutanix Cloud Infrastructure into Nutanix Enterprise AI in a single operational view, giving infrastructure and platform teams the visibility they need to understand and manage the full environment powering agentic AI. Teams can correlate AI workload behavior with the underlying compute, GPU, storage, and orchestration resources required to run it, connecting signal across every layer into a coherent picture of system performance.
Virtana addresses this new operational requirement by delivering:
Real-time GPU telemetry, including utilization, memory, power draw, temperature, and health across distributed clusters
Detection of idle and underutilized GPUs to reduce waste and cost and improve AI infrastructure efficiency
Workload-to-GPU correlation across training, inference, and agent-driven workflows, connecting AI service behavior to infrastructure usage and cost
Token-level visibility into throughput, latency, and resource demand so teams can better understand cost and performance under concurrency
Early identification of thermal, power, and reliability risks before they affect production AI services
Performance analysis for multi-node, multi-GPU environments supporting dynamic agentic workloads
A unified operational view across Nutanix AHV, Nutanix Enterprise AI, Kubernetes orchestration, NVIDIA GPU clusters, and distributed AI workflows, enabling end-to-end observability across the AI factory
Virtana will demonstrate AI Factory Observability for Nutanix Agentic AI at Nutanix .NEXT 2026 in the AI Pavilion at the .NEXT Solutions Expo. Live demonstrations will show how organizations can connect agent behavior to full infrastructure context—linking token generation, workload performance, and GPU utilization across Nutanix environments, from model pipelines to distributed infrastructure dependencies.
About Virtana
Virtana delivers a unified observability platform for hybrid and multi-cloud environments, with full-stack AI observability spanning applications, services, data pipelines, GPUs, CPUs, networks, and storage. Powered by high-fidelity data and designed for agentic AI systems, Virtana provides end-to-end observability across infrastructure and AI workloads—correlating performance, cost, and system behavior in real time.
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