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Virtana Adds Agentic AI Capabilities

Virtana introduced powerful new autonomous agents as part of its expanding agentic AI strategy. 

Built for today's sprawling, hypercomplex, hyperscale environments, these agents detect issues before they surface, interpret impact instantly, and address problems far beyond what humans can manage in real time. This not only results in greater resilience and improved operational efficiency, but it also enables IT professionals to focus on meeting business-critical SLAs and delivering meaningful business outcomes.

"Technology environments are advancing faster than legacy observability solutions can support," said Paul Appleby, Chief Executive Officer at Virtana. "Agentic AI demands unified, trustworthy data to operate with accuracy and accountability. By capturing telemetry across every layer of IT operations and every part of the enterprise, Virtana enables AI agents to act with full context and deliver coordinated, predictive operations rather than reactive effort. Reaching this milestone signals a shift in how enterprises govern, optimize, and scale their operations within the digital economy."

Virtana invested in a unified platform that brings together 20,000+ distinct metrics spanning the full digital estate. Because the agents operate on such rich, interconnected data, they can recognize patterns, understand dependencies, and make decisions that reflect the full context of the environment.

By aligning autonomous decisions to digital SLAs, Virtana helps enterprises boost productivity and efficiency, strengthen cyber resilience and data protection, and continuously optimize workloads. It also improves asset utilization and cost management and accelerates innovation across digital and AI services in a world where infrastructure is too complex and too dynamic for human-only operations.

"We've been applying AI to enterprise infrastructure for over a decade, long before AI became a buzzword." said Amitkumar Rathi, Senior Vice President of Product and Engineering at Virtana. "We built a modern AI architecture for enterprise observability that unifies topology, telemetry, business impact, and cost into a single reasoning fabric. Our agents act with confidence because they understand the full context and can tune policies, orchestrate remediation, and surface the right trade-offs automatically. This is the future of operating within complex digital economies."

These agentic AI and autonomous capabilities move Virtana beyond legacy observability into an active, continuous control plane for digital business. Built on a unified understanding of performance, cost, and topology, Virtana's agents do not just describe what is happening. Instead, they constantly search for emerging risks, pinpoint their impact across services, and translate that into natural-language briefings, so operators can talk to their infrastructure and approve actions instead of digging through charts. This marks a clear break from the first generation of AIOps, replacing slow, manual analysis with intelligent, autonomous operations that are purpose-built for modern, cloud-native architectures. 

In practical terms, Virtana's latest autonomous agents transform how teams handle the core disciplines of IT operations, and deployments have shown improvements of up to:

  • 95% faster root cause isolation: Autonomous agents correlate signals across every layer, surface root cause in real-time, and propose clear actions, replacing slow, manual digging through logs and dashboards.
  • 80% reduction in alert noise: Agents learn normal behavior, suppress noise, and highlight only the issues that truly threaten SLAs and revenue, instead of forcing teams to constantly tune thresholds and chase alert storms.
  • 25% lower infrastructure and tooling costs: Agents continuously optimize workload placement and utilization, turning cost pressure into a structural advantage, rather than relying on engineers to right-size and decommission resources in ad hoc bursts.
  • 75% faster policy changes: Agents turn plain-language intent into enforceable policies, monitor adherence, and flag risks before they become incidents, instead of hand-crafting brittle policy files that are difficult to maintain.
  • 50% faster onboarding: Knowledge and onboarding agents act as always-on coaches, so every engineer operates like a seasoned expert, replacing long ramp times spent learning tools and hunting tribal knowledge.

These agentic AI capabilities turn observability from a passive monitoring layer into an active economic engine for digital business. By automating the core disciplines of IT operations—root cause, noise reduction, cost optimization, governance, and expertise—Virtana gives enterprises a practical path to protect critical SLAs, convert cost pressure into competitive advantage, and consolidate onto a single autonomous control plane that is built for modern, cloud-native architectures.

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Virtana Adds Agentic AI Capabilities

Virtana introduced powerful new autonomous agents as part of its expanding agentic AI strategy. 

Built for today's sprawling, hypercomplex, hyperscale environments, these agents detect issues before they surface, interpret impact instantly, and address problems far beyond what humans can manage in real time. This not only results in greater resilience and improved operational efficiency, but it also enables IT professionals to focus on meeting business-critical SLAs and delivering meaningful business outcomes.

"Technology environments are advancing faster than legacy observability solutions can support," said Paul Appleby, Chief Executive Officer at Virtana. "Agentic AI demands unified, trustworthy data to operate with accuracy and accountability. By capturing telemetry across every layer of IT operations and every part of the enterprise, Virtana enables AI agents to act with full context and deliver coordinated, predictive operations rather than reactive effort. Reaching this milestone signals a shift in how enterprises govern, optimize, and scale their operations within the digital economy."

Virtana invested in a unified platform that brings together 20,000+ distinct metrics spanning the full digital estate. Because the agents operate on such rich, interconnected data, they can recognize patterns, understand dependencies, and make decisions that reflect the full context of the environment.

By aligning autonomous decisions to digital SLAs, Virtana helps enterprises boost productivity and efficiency, strengthen cyber resilience and data protection, and continuously optimize workloads. It also improves asset utilization and cost management and accelerates innovation across digital and AI services in a world where infrastructure is too complex and too dynamic for human-only operations.

"We've been applying AI to enterprise infrastructure for over a decade, long before AI became a buzzword." said Amitkumar Rathi, Senior Vice President of Product and Engineering at Virtana. "We built a modern AI architecture for enterprise observability that unifies topology, telemetry, business impact, and cost into a single reasoning fabric. Our agents act with confidence because they understand the full context and can tune policies, orchestrate remediation, and surface the right trade-offs automatically. This is the future of operating within complex digital economies."

These agentic AI and autonomous capabilities move Virtana beyond legacy observability into an active, continuous control plane for digital business. Built on a unified understanding of performance, cost, and topology, Virtana's agents do not just describe what is happening. Instead, they constantly search for emerging risks, pinpoint their impact across services, and translate that into natural-language briefings, so operators can talk to their infrastructure and approve actions instead of digging through charts. This marks a clear break from the first generation of AIOps, replacing slow, manual analysis with intelligent, autonomous operations that are purpose-built for modern, cloud-native architectures. 

In practical terms, Virtana's latest autonomous agents transform how teams handle the core disciplines of IT operations, and deployments have shown improvements of up to:

  • 95% faster root cause isolation: Autonomous agents correlate signals across every layer, surface root cause in real-time, and propose clear actions, replacing slow, manual digging through logs and dashboards.
  • 80% reduction in alert noise: Agents learn normal behavior, suppress noise, and highlight only the issues that truly threaten SLAs and revenue, instead of forcing teams to constantly tune thresholds and chase alert storms.
  • 25% lower infrastructure and tooling costs: Agents continuously optimize workload placement and utilization, turning cost pressure into a structural advantage, rather than relying on engineers to right-size and decommission resources in ad hoc bursts.
  • 75% faster policy changes: Agents turn plain-language intent into enforceable policies, monitor adherence, and flag risks before they become incidents, instead of hand-crafting brittle policy files that are difficult to maintain.
  • 50% faster onboarding: Knowledge and onboarding agents act as always-on coaches, so every engineer operates like a seasoned expert, replacing long ramp times spent learning tools and hunting tribal knowledge.

These agentic AI capabilities turn observability from a passive monitoring layer into an active economic engine for digital business. By automating the core disciplines of IT operations—root cause, noise reduction, cost optimization, governance, and expertise—Virtana gives enterprises a practical path to protect critical SLAs, convert cost pressure into competitive advantage, and consolidate onto a single autonomous control plane that is built for modern, cloud-native architectures.

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Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

In MEAN TIME TO INSIGHT Episode 20, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA presents his 2026 NetOps predictions ... 

Today, technology buyers don't suffer from a lack of information but an abundance of it. They need a trusted partner to help them navigate this information environment ...

My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

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