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Virtana has launched a new Application Observability offering designed to achieve what legacy APM tools have failed to deliver: automatically tracing performance failures from application code down through infrastructure, networks, storage, and AI workloads to surface evidence-backed root cause without manual correlation. Built for autonomous operations at scale, the solution redefines the application as a system rather than just software, enabling operators and AI agents to identify the true limiting dependency across hybrid environments.
Virtana launched a new AI-native Application Observability capability that correlates performance issues across the full stack, from code and services to infrastructure, networks, storage, and AI workloads.
New Virtana research reveals 52% of IT practitioners face persistent visibility gaps despite median annual observability spending exceeding $800,000 per enterprise.
The solution automatically surfaces system-level root cause by unifying application telemetry with full-stack context, eliminating manual correlation across fragmented tools.
Key features include an AI-native agentic investigation layer, a system dependency graph foundation, and Kubernetes-aware observability.
The platform integrates with leading AI assistants through Virtana's MCP Server, enabling natural language analysis grounded in operational context.
The new capability is available immediately, targeting organizations running mission-critical applications across complex hybrid environments.
Legacy APM platforms were built for a world where applications were largely standalone code. Today, mission-critical systems such as airline reservations, payment processing, healthcare delivery, and emergency dispatch are complex ecosystems spanning software, services, infrastructure, and AI workloads. When performance is limited by storage behavior, network paths, Kubernetes resource pressure, or GPU contention, traditional APM exposes symptoms while teams manually correlate cause across fragmented tools.
New Virtana research, "AI Is Breaking Human-Managed Operations," reveals that 52% of IT practitioners report persistent visibility gaps despite median annual observability spending exceeding $800,000 per enterprise, according to Gartner's April 2025 report. A subset of these enterprises spends more than $10 million annually on a single vendor. The findings highlight that more tools have not solved the problem, pointing to the need for a different architectural approach.
"Mission-critical applications such as airline reservation systems, payment processing systems, health care delivery systems, and emergency dispatch are no longer just code, but complex systems spanning software, services, infrastructure, and AI workloads," said Paul Appleby, CEO of Virtana. "At this scale and complexity, legacy APM focused on code and human-only operations is no longer a credible way to understand how applications behave. Our research shows that this trajectory will accelerate as AI workloads, new dependencies, greater infrastructure strain, and failure modes that legacy tools cannot explain continue to multiply. The only viable path forward is open, agentic, system-level observability."
Virtana's new Application Observability capability delivers visibility into request flows, service interactions, latency, and errors, and automatically correlates those signals to downstream dependencies across infrastructure, storage, network, and AI workloads. By unifying application telemetry with full-stack observability, the platform fundamentally changes incident response, enabling teams to immediately determine whether performance issues originate in application code or downstream constraints.
"As a leading AI-powered technology solutions provider supporting more than 6,000 CIOs across public sector and enterprise organizations, we cannot operate with visibility that stops at the code," said Doug Syer, Chief Engineer for AI Monitoring and Observability at NWN. "Modern applications are distributed systems, and performance constraints frequently originate in infrastructure, network, or platform layers that traditional APM was never designed to see. Virtana Application Observability offers true system-level visibility, correlating signals across the full stack, enabling the immediate transition from symptoms to evidence-backed root cause."
The new Application Observability capability is built on several foundational technologies designed for autonomous operations at scale.
The platform provides AI-native, agentic investigation and automation, enabling natural language analysis grounded in operational context through Virtana's MCP Server. It is compatible with leading AI assistants including ChatGPT, Claude, Gemini, and Copilot. A System Dependency Graph foundation continuously maps relationships across applications, services, Kubernetes workloads, infrastructure, networks, storage, and AI platforms, providing the system-level context that enables automated reasoning.
AI-powered root cause analysis automatically identifies where latency, failures, or constraints originate across the entire stack and prioritizes the most likely limiting dependency with supporting evidence. The platform combines end-to-end transaction tracing, intelligent log correlation, and synthetic monitoring to detect user-impacting issues and trace root causes from the user layer to infrastructure. Kubernetes-aware observability provides native visibility into clusters, workloads, nodes, and resource contention across container environments.
"At modern scale, root cause rarely exists inside a single service or trace. It emerges from interactions between application runtime behavior, Kubernetes orchestration, infrastructure capacity, and network dynamics," said Amitkumar Rathi, Chief Product Officer at Virtana. "Legacy observability was built for a world where applications were just code. Today's systems are dynamic, distributed, and increasingly driven by AI, and fragmented tools cannot keep up. We built Virtana to see the entire system and correlate traces, logs, topology, and infrastructure telemetry into one operational context, allowing engineers and AI agents to act on it instead of chasing symptoms across disconnected signals."
When an application issue appears, Virtana traces it across the full system, revealing how services, infrastructure, networks, and AI workloads interact to create the problem. Instead of debating symptoms, teams receive evidence-backed guidance grounded in real operational context, accelerating triage and minimizing downtime.
About Virtana
Virtana delivers the deepest and broadest observability platform for hybrid and multi-cloud, with full-stack AI observability spanning applications, services, data pipelines, GPUs, CPUs, networks, and storage. Powered by high-fidelity data and agentic AI, Virtana provides unmatched visibility across end-to-end IT services and AI workloads, correlating health, performance, cost, and user impact in real time. With advanced event intelligence and autonomous insight generation, Virtana delivers clarity no other provider can match. Trusted by Global 2000 enterprises and public sector organizations, Virtana helps IT operations and DevOps teams reduce risk, strengthen resilience, improve efficiency, and modernize with confidence across multi-cloud, on-premises, and edge environments.
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