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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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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...