Virtana has been named a winner of SiliconANGLE’s 2026 TechForward Awards in the AI-Driven Cloud Infrastructure category, putting its agentic observability platform in a market increasingly focused on connecting AI workloads with the infrastructure they depend on. The company says its platform correlates more than 25,000 telemetry signals across applications, infrastructure, networks, storage, Kubernetes and AI environments to provide operational context for AI agents.
Enterprise infrastructure is becoming harder to monitor as workloads spread across public clouds, on-premises systems, hybrid environments and AI-specific infrastructure. At the same time, organizations are beginning to give AI agents greater responsibility for diagnosing incidents and taking operational actions.
That convergence is creating demand for observability platforms that do more than collect alerts. Virtana has now been recognized by SiliconANGLE’s 2026 TechForward Awards in the AI-Driven Cloud Infrastructure category, highlighting the company’s approach to using operational telemetry as context for AI-driven infrastructure management.
The award recognizes technologies across AI, security, cloud, data platforms and blockchain/crypto. SiliconANGLE says its TechForward entries are evaluated by a panel including industry analysts, journalists, venture capitalists and technology practitioners, with criteria including technical innovation, market differentiation and customer impact.
Virtana describes its platform as an agentic observability system for hybrid and multicloud environments. According to the company, it continuously collects and correlates more than 25,000 telemetry signals across applications, infrastructure, services and AI systems.
The objective is to give AI agents a broader view of how different parts of an enterprise environment interact. Rather than treating an application failure, network problem, storage constraint or Kubernetes event as separate alerts, the platform attempts to establish relationships between them and identify the underlying cause.
That approach matters as enterprises move from monitoring AI infrastructure to allowing AI systems to participate in IT operations.
Traditional observability platforms generally provide telemetry, dashboards, alerts and analysis that human operators use to investigate incidents. Agentic observability adds an automation layer: an AI system can interpret operational information, recommend a response and, where governance allows, execute remediation.
Gartner’s 2026 research reflects this shift. The firm says observability platforms are increasingly incorporating AI-driven capabilities, while its research on agentic AI and observability identifies a move from explanation toward action, including automated workflows and self-healing systems.
Gartner also predicts that 40% of organizations deploying AI will use dedicated AI observability tools by 2028 to monitor model performance, bias and outputs. The firm says AI observability needs to extend beyond conventional infrastructure monitoring because AI systems introduce issues such as model drift, opaque decision-making and output-related risks.
The infrastructure side of that equation is becoming more demanding as well. Gartner forecasts worldwide AI-optimized infrastructure-as-a-service spending will reach about $42.3 billion in 2026, up 96.4% from 2025. It also expects global spending on AI inference to reach $23.3 billion this year, exceeding the $19 billion projected for AI training.
That shift toward inference has practical consequences for observability. Production AI workloads need to remain available while drawing resources from increasingly complex infrastructure. Enterprises therefore need visibility into not only whether a model or application is functioning, but also how GPUs, networks, storage, cloud resources and supporting services affect performance and cost.
Virtana says its platform is designed around this full-stack view. The company claims its architecture can identify constraints affecting performance, cost and failures and provide that information to its own agents. It also says open interfaces, including the Model Context Protocol (MCP), allow external AI agents and large language models to access its operational intelligence.
MCP is becoming part of a wider effort to standardize how AI applications connect to external tools and data. In an observability environment, that can allow an agent operating outside the monitoring platform to retrieve infrastructure context before making a recommendation or taking an action.
The technology also fits into a broader movement toward enterprise context layers. Gartner’s July 2026 research argues that agentic AI requires a robust context layer to ground models in enterprise realities and support reliable, governed and cost-conscious operation.
The competitive landscape includes established observability vendors such as Datadog, Dynatrace, New Relic, Elastic, Splunk and others, alongside cloud providers and newer AI-focused monitoring platforms. Gartner’s July 2026 observability research identifies AI/LLM observability, automated response, agentic AI, telemetry management and cost optimization among the capabilities relevant to the market.
Virtana’s distinction is its emphasis on connecting infrastructure context directly to autonomous operational workflows. Whether that approach can consistently reduce incident resolution times or operating costs will depend on the quality of telemetry, the accuracy of system relationships and the controls organizations place around automated actions.
That governance question is important. Giving an AI agent permission to modify production infrastructure is materially different from using AI to summarize an incident. Enterprises need mechanisms that constrain actions, preserve human oversight where required and provide an audit trail of what an agent observed and why it acted.
For Virtana, the TechForward recognition arrives as observability increasingly becomes part of the architecture for AI-powered enterprise operations. The underlying market shift is broader than any individual vendor: as AI systems become operational participants, infrastructure telemetry is becoming a source of context that those systems need to reason effectively.
Market Landscape
Enterprise observability is evolving alongside hybrid cloud, AI infrastructure and agentic AI. Gartner’s June 2026 guidance says multicloud observability must accommodate the increasing use of AI infrastructure, while its research on AI observability points to specialized monitoring for model behavior, performance and risk.
AI infrastructure spending is also expanding rapidly. Gartner projects AI-optimized IaaS spending at approximately $42.3 billion in 2026 and expects inference spending to exceed training spending this year.
This creates a market in which observability increasingly spans conventional applications, cloud infrastructure, GPUs, AI models, Kubernetes, networks and automated remediation. The emerging architecture combines telemetry collection with AI-powered analysis, enterprise context and governed action.
Top Insights
- Virtana won SiliconANGLE’s 2026 TechForward Award in AI-Driven Cloud Infrastructure for its agentic observability approach.
- The platform says it correlates more than 25,000 telemetry signals across applications, infrastructure, networks, storage, Kubernetes and AI environments.
- Gartner expects 40% of organizations deploying AI to adopt dedicated AI observability tools by 2028.
- AI inference spending is projected to surpass training spending in 2026, increasing the need for production-scale infrastructure visibility.
- Agentic observability is shifting monitoring from passive alerting toward AI-assisted diagnosis, recommendations and governed remediation.
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