Closing the Observability Gap with Agentic AI

Executives may not perceive the same risks

In a world where IT systems grow ever more intricate, a major outage of a critical application should never trigger an inter‑departmental blame game. Still, the reality in many large companies is that network, application, and compute teams often work in isolation, using old, fragmented monitoring tools. Each tool offers only a narrow view, turning root‑cause identification into a laborious task that keeps vital services offline for customers.

As organizations expand their use of artificial intelligence, hybrid infrastructures, and sovereign cloud environments, the pressure on operational visibility rises exponentially. Systems that support critical services become increasingly interconnected. The need for high resilience, operational efficiency, and strict governance becomes imperative. While AI agents are beginning to shoulder some operational load, they rely on data from legacy monitoring systems. An AI agent that reasons on incomplete data will see the same partial picture that human operators see, perpetuating existing gaps.

Executives may not perceive the same risks that frontline teams experience. Virtana studies show a significant gap between what boards believe they know about their IT environment and what first‑line operators actually experience. In an interview with Tim Phillips of The Register, Paul Appleby, CEO of Virtana, examined the limits of traditional monitoring and explained how agentic observability can bring new clarity to corporate infrastructure.

The discussion also highlights the growing importance

Appleby stresses that fragmented monitoring from the past leaves IT teams in trouble when trying to separate an incident’s root cause from its superficial symptoms. The proposed solution is agentic observability, a method that correlates real‑time telemetry from applications, cloud, infrastructure, networks, and AI systems. This integration gives AI agents the context needed to accelerate root‑cause analysis and remediation processes.

The discussion also highlights the growing importance of observability in the context of data sovereignty and dedicated AI infrastructure. As companies invest heavily in AI factories and increasingly complex workloads, Appleby explores how organizations can maintain cross‑stack visibility. The goal is to strengthen governance and address operational risks before they disrupt critical services. For IT leaders, this dialogue offers a valuable perspective on moving from a classic model that proves departmental innocence to a model focused on rapid resolution, preventive observability, and more resilient operations.

The interview raises a crucial question for executives: do investments in governance and operational controls keep pace with the speed at which AI infrastructure and services become essential to business? By exploring how agentic observability closes this visibility gap, companies can strengthen their IT resilience. Contributed by Virtana.