Virtana announced the latest version of its Model Context Protocol (MCP) Server, bringing full-stack enterprise visibility directly to AI agents and LLMs so machines can understand enterprise operations as complete systems rather than isolated signals.
Opening the Virtana platform to a broad ecosystem of AI agents, automation systems, and large language models (LLMs), such as OpenAI's ChatGPT, Anthropic's Claude, Google Gemini, and Microsoft Copilot, enables AI to execute full-stack decisions across end-to-end enterprise environments, advancing observability from fragmented monitoring into autonomous, self-managing environments.
With the Virtana MCP Server, an AI copilot uses natural-language queries to analyze a system dependency graph, allowing the LLM to understand the application as a connected system rather than isolated code or signals, revealing fine-grained relationships and operational context across the stack.
With the Virtana MCP Server, an AI copilot uses natural-language queries to analyze a system dependency graph, allowing the LLM to understand the application as a connected system rather than isolated code or signals, revealing fine-grained relationships and operational context across the stack.
Built on Virtana's patented full-stack optimization architecture, the platform powers a system dependency graph, a dynamic map that builds a structured understanding of how applications, services, infrastructure, and AI workloads interact across the enterprise.
"The shift to AI-driven operations fundamentally changes what observability must deliver. It is no longer enough to surface signals; platforms must provide a structured understanding of the system itself," said Amitkumar Rathi, Chief Product Officer at Virtana. "Virtana builds a unified dependency graph that derives operational context across hybrid environments, and the MCP Server exposes that model as a standard interface for AI agents and LLMs. This enables a new operational paradigm where AI systems can analyze, prioritize, and act across the full stack based on real system relationships rather than isolated alerts."
Virtana normalizes operational telemetry into a unified system dependency graph. This dependency-aware representation of distributed applications is surfaced through MCP, enabling AI agents powered by leading large language models, including ChatGPT, Claude, and Gemini, to interact directly with structured operational context.
Rather than layering conversational interfaces onto legacy monitoring stacks, Virtana treats natural language as an expression of intent. Virtana's MCP Server translates that intent into structured interactions with the dependency graph, allowing AI agents to retrieve grounded data, analyze relationships, and reason across the full system.
The Virtana MCP Server enables AI agents to:
- Query Full-Stack Context in Natural Language: AI agents and leading LLMs, including ChatGPT, Claude, Gemini, Microsoft Copilot, or any compatible model, can ask "which services are affected by storage latency in region X" and receive structured responses that traverse infrastructure, orchestration, and application layers, without pre-built queries or domain expertise, enabling natural-language telemetry exploration across unified observability data.
- Autonomous Root Cause Analysis and Dependency Reasoning: Virtana's system dependency graph provides AI agents with live topology awareness, enabling autonomous correlation of signals, dependencies, and historical patterns to identify probable root cause and prioritize actions based on downstream impact rather than isolated symptoms.
- Analyze System Behavior Holistically: AI agents can correlate signals across network, infrastructure, and application layers to understand how distributed systems interact across hybrid and multi-cloud environments, eliminating visibility gaps created by fragmented observability tools through context-aware enrichment based on learned system behavior.
- Recommend Optimizations Based on Dependency-Aware Understanding: Built on Virtana's patented optimization architecture, AI agents can leverage dependency-aware insights to recommend intelligent actions grounded in real system structure rather than isolated metrics.
- Drive Automation Through Open Execution Frameworks: Automation platforms such as Ansible, Terraform, and other orchestration tools can connect through Virtana's MCP Server to execute workflows based on AI-generated insights and decisions.
With the Virtana MCP Server, AI agents understand enterprise-wide system dependencies, from infrastructure to applications, moving beyond reactive monitoring to deliver intelligent recommendations based on real operational context.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.