Operations | Monitoring | ITSM | DevOps | Cloud

Fragmented telemetry limits what AI can understand. When logs, metrics, and traces remain siloed, AI sees individual signals instead of the full story. That can lead to incorrect conclusions and unexpected outcomes. This is where AI observability matters. Virtana connects telemetry across the stack, giving AI the context it needs to correlate signals, understand dependencies, and identify what is really happening.

AI is changing how organizations operate, but one thing has not changed: critical services cannot fail. Whether it is financial markets, healthcare, or other mission critical environments, organizations need observability that delivers value quickly, not weeks or months later. In this clip with theCube, Virtana CEO Paul Appleby explains how Virtana combines high fidelity telemetry with AI-driven intelligence to discover dependencies, correlate relationships, and deliver actionable insights within hours.

Anomaly Detection Isn’t Agentic AI. Detecting anomalies is table stakes. That’s pattern recognition. Agentic AI is different. It understands dependency chains, explains cause and recommends action. There’s a big gap between: “Something changed.” and “Here’s what broke and what to do next.” If your AI stops at anomaly scoring, you’re still doing the thinking.

Most enterprises have observability tools. What they often lack is a shared view between application and infrastructure teams. When application performance degrades, finding the root cause can be slow because the data lives in separate silos. Virtana brings application observability and infrastructure intelligence together in a single platform, helping teams identify issues faster, collaborate more effectively, and shift from reactive troubleshooting to proactive operations.

If your incident response still depends on engineers stitching alerts together… You don’t have observability. You just have dashboards. Correlation has to be automatic, contextual, and system-aware, or it becomes expensive noise.

AI can't reason about what it can't see because AI requires evidence or the observed signals. Correlation requires co-occurrence. If it is not in the data set it doesn't exist to the machine. So the key insight is if there are omitted variables, there are going to be blind spots forever.

AI isn’t about replacing engineers. It’s about leverage. The teams that win will be the ones that: Triage incidents faster Correlate signals automatically Reduce manual investigation Automate repetitive operational work In observability, that means asking: AI won’t eliminate expertise, it amplifies it. The real risk isn’t AI taking your job. It’s competitors using AI to operate at a speed and efficiency you can’t match.

Most teams don’t have a data problem. They have a correlation problem. When visibility is fragmented:→ Marketing sees conversion drop→ Engineering sees API latency So the wrong call gets made. Example: Checkout drops → pricing gets blamed → discounts applied. Reality: a backend API timeout was killing transactions. That’s what happens when you can’t connect: user impact (what) to system behavior (why)

Most “application issues” don’t start in the code. They start somewhere deeper in the delivery system. If you can’t see beyond the trace, you’re only seeing half the story.