The "Single Pane of Glass" Is Dead — What Network Teams Actually Need Is Intelligence
Summary
The infrastructure industry spent two decades chasing a single pane of glass. The future looks different: domain-expert AI platforms that reason deeply within their own data, connected through tool chaining when problems cross boundaries.
For two decades, every infrastructure vendor has made the same promise: buy our platform, and you’ll get a single pane of glass across your entire environment. One dashboard. One view. Total visibility. I’ve even been guilty of saying those words during a Kentik demo to describe our approach.
It hasn’t worked. 85% of enterprise IT leaders report that true unified observability remains elusive despite years of investment in monitoring and management tools. The average enterprise now manages 75 or more security and monitoring tools simultaneously. Each one was supposed to simplify. Together, they’ve created a sprawl that makes it harder, not easier, to understand what’s actually happening.
The industry has been solving the wrong problem. The true bottleneck was never the number of screens an engineer had open. It was the gap between seeing data and understanding what it means.
Why dashboards were never going to be enough
The appeal of the single pane of glass is obvious. Executives want simplicity. Operations teams want speed. And the pitch sounds logical: If you can see everything in one place, you can fix problems faster.
In practice, dashboards become wallpaper. Teams walk past them without noticing incidents in progress. The charts look impressive to visitors, but they don’t change how problems are actually diagnosed. When a critical application slows down, the generalist platform’s high-level alert that “it’s a network problem” usually marks the beginning of a troubleshooting effort, not the end.
The reason is structural. A dashboard can show you that something is wrong. It can surface an alert, display a metric turning red, or highlight a latency spike on a chart. What it cannot do is investigate. It can’t correlate a routing change in BGP with a shift in cloud egress patterns and a simultaneous uptick in synthetic test failures to tell you that your traffic to Southeast Asia is now taking a suboptimal path because of a peering dispute you didn’t know about.
That kind of reasoning requires context, domain expertise, and the ability to move across data sources in a purposeful sequence. Dashboards aggregate data. They don’t reason about it.
The specialization reality
The industry didn’t consolidate. It specialized.
Most enterprises today run purpose-built platforms across every layer of the stack. APM teams rely on Datadog, Dynatrace, New Relic, or Sumo Logic. Security teams have their own toolchains. And network teams cobble together a mix of legacy monitoring systems, cloud-native dashboards, and manual troubleshooting.
There was a period where the answer was supposed to be dumping everything into a shared data lake and querying across it. That trend has largely faded because it turns out that aggregating data in one place doesn’t solve the expertise problem. A data lake full of flow records, BGP updates, and SNMP metrics becomes a data swamp if nothing on top of it knows what those signals mean in context.
What each domain actually needs is a platform that deeply understands its own data and can reason about it with domain expertise. The APM team needs a tool that understands application traces and service dependencies. The network team needs a tool that understands traffic flows, routing decisions, peering economics, and cloud path behavior.
The problem is that these platforms have historically been islands. Each one can get you partway to the root cause within its own domain. None of them can hand off context to another domain’s tooling in an intelligent, automated way. When a NOC analyst determines the problem isn’t the network and an application team determines it isn’t the app, the conversation stalls in the gap between them.
What comes after the dashboard
The shift happening now isn’t toward one platform that does everything. It’s toward specialized platforms that are genuinely intelligent within their domains, connected by AI-driven interoperability.
Within the network domain, Kentik Co-founder and CEO Avi Freedman has described this as the evolution from observability to network intelligence: the ability to understand what’s happening, why it matters, and what to do next. That third element is what separates intelligence from monitoring. A dashboard can show you a traffic spike. Intelligence can tell you whether that spike is a DDoS attack, a legitimate surge from a content delivery event, or an artifact of a routing change, and recommend the appropriate response for each.
This is what Kentik AI Advisor was designed to deliver. When an engineer asks a question in natural language, AI Advisor doesn’t just search for a matching chart. It builds an investigation plan, autonomously queries across flow records, device metrics, cloud paths, BGP routing, and synthetic tests, then presents findings with the supporting evidence visible at every step.
The difference is meaningful in daily operations. A NOC analyst who previously needed to escalate a complex issue to a senior network engineer can now get AI Advisor to walk through the same investigative steps that a senior engineer would have followed. A cloud team trying to understand why egress costs spiked last month can ask the question directly and receive an answer grounded in actual traffic data, without needing to know which Kentik product to query or how to build the right filter.
But here’s the larger point: What’s true for network intelligence is becoming true across the entire stack. Every operational domain is building AI that can reason deeply within its own telemetry. The real unlock comes when those systems can talk to each other.
The future is tool chaining, not tool consolidation
This is where emerging standards like MCP (Model Context Protocol) and A2A (Agent-to-Agent) protocols change the game.
Instead of forcing all data into a single platform that understands nothing deeply, the future looks like specialized AI agents that are experts in their own domains and can chain together when a problem crosses boundaries. An application performance agent detects elevated error rates and determines the issue isn’t in the application layer. It hands off context to a network intelligence agent, which investigates the traffic path and discovers a congestion event at a peering point. The network agent passes its findings back, and the system presents a unified root cause analysis with remediation options.
No single platform has built that end-to-end answer. But the chain of specialized, intelligent agents did.
This is the real answer to the executive question: “When something goes wrong, can your platform figure out why and tell me what to do about it?” The honest answer is that no single platform can do this across every layer. But an ecosystem of intelligent, domain-expert platforms that can share context and coordinate investigations through AI tool chaining can get closer to that vision than any single pane of glass ever could.
What this means for your tooling strategy
For executives evaluating infrastructure tooling, this reframes the decision. Stop asking vendors whether they can show you everything in one place. Start asking two different questions.
First: Within its domain, can this platform reason about problems the way an expert would? Can it investigate, not just alert? Kentik built its platform around exactly this standard. The Kentik Data Engine processes roughly one trillion telemetry points per day, unifying flow, device, cloud, and internet data into a single queryable foundation. AI Advisor sits on top of that foundation, turning raw network observability into contextual intelligence that any team member can access.
Second: Can this platform participate in a broader ecosystem? Can it expose its intelligence to other tools through APIs, MCP, or agent-to-agent protocols? The platforms that will matter most in the next few years won’t be the ones that try to replace everything else. They’ll be the ones that are deeply expert in their domain and open enough to chain with everything else.
The single pane of glass promised simplicity through consolidation. It never delivered. What comes next is more promising: specialized intelligence within each domain, connected by AI across them. The gap between staring at a dashboard and knowing what to do next finally closes, not with a better dashboard, but with systems that can actually think.
