Insights from AI Infra Summit 2026
Most AI infrastructure teams do not have a shortage of data. They have a context problem.
That was the clearest takeaway from three days of conversations at AI Infra Summit 2026. We spent the event talking with the people building and operating enterprise AI environments. While the discussions covered everything from existing observability tools to agent governance and air-gapped deployments, they kept coming back to the same challenge.
When an AI workload slows down, the symptom may appear in the model or application while the underlying cause may sit somewhere else entirely: an oversubscribed GPU, a slow data pipeline, storage throughput, network congestion, Kubernetes configuration or an upstream service. Teams can see that something is wrong. Connecting the signals across the full environment and determining what to do next is still much harder than it should be.
The conversation rarely stopped at the GPU
A conversation might start with GPU utilization, then move almost immediately to storage throughput, network congestion, Kubernetes configuration or a slow data pipeline. That makes sense: AI performance depends on the full execution system, not one component viewed in isolation.
A GPU dashboard can tell an operator that utilization dropped. However, looking at a dashboard alone won’t uncover whether the underlying issue is compute, data movement, storage, networking or an upstream service. Teams want observability that connects workload behavior to the infrastructure and services supporting it, so they can understand the whole story instead of chasing symptoms one layer at a time.
Teams have tools but still struggle to get answers
We heard plenty of familiar names in the booth: Datadog, Dynatrace, Grafana, New Relic and a range of homegrown platforms. The question was not whether those tools collect useful data. It was how to bring their signals together, understand dependencies across domains and move from an alert to a root cause the team can defend.
Some teams are trying to consolidate custom telemetry. Others are building their own reporting layers or sharing data across tools that were never designed to operate as one system. What they need is not another disconnected dashboard. They need context through correlation that shows how applications, AI workloads and infrastructure affect one another.
Agentic operations prompted practical questions
People were interested in agentic operations, but they were not asking only about automation. They wanted to know how diagnostic agents reason, how teams can track what an agent did and what guardrails belong around actions in production. Security and governance came up in the same breath, especially in environments where a recommendation can affect critical infrastructure.
Those questions get to the heart of our Agentic Observability approach. Virtana applies an Observe, Reason and Act model across the full environment. Agents observe high-fidelity signals and dependencies, reason with system-wide context to identify likely causes, and support governed action with clear explanations and human control. The point is not automation for its own sake. It is helping operators make better decisions without giving up oversight.
Hybrid and secure environments are still the reality
Another thing the booth conversations made clear: AI infrastructure is not settling into one standard architecture. The teams we met are working across public cloud, private cloud, on-premises systems, colocation facilities and specialized AI infrastructure. Some are also supporting regulated, sovereign or air-gapped environments where data location and access controls are non-negotiable.
Observability has to work within those boundaries. Teams need consistent system context without moving sensitive data or control outside approved environments. They also need one operational view as workloads and dependencies cross infrastructure types.
The ecosystem matters too
We also had several conversations that moved beyond a single product or deployment. Cloud and neocloud providers, systems integrators, hardware vendors, security companies and AI software providers are all looking for ways to give customers a more complete view of their environments. Marketplace integrations, joint solutions and technology partnerships came up naturally because no one vendor owns the entire AI execution system.
That makes open integration and shared context part of the operating model, not an afterthought. Customers need to diagnose problems across technology and organizational boundaries, and the ecosystem has to make that easier.
What we are taking away
Walking away from the summit, our biggest takeaway is straightforward: the next phase of AI infrastructure operations will not be won by collecting more telemetry. Teams need to connect what is happening across the environment, explain why it is happening and determine the right action with the right controls in place.
That is the problem the Virtana Agentic Observability Platform is built to solve. By combining deep infrastructure telemetry, full-stack dependency context and agentic reasoning, Virtana helps teams move from fragmented monitoring toward a system of action for applications, services and AI workloads. After the conversations we had in the booth, the need for that system-level context feels more immediate than ever.
Learn more in our white paper, the Virtana Agentic Observability Platform, about how system-aware context helps teams and AI agents observe, reason and act across complex IT and AI environments.
Phil Hyun
Product Marketing Manager
