Closing the Internet Gap to Enable Autonomous IT with Edwin AI + Catchpoint
Autonomous IT only works when AI can see beyond your infrastructure. Learn how Edwin AI plus Catchpoint closes the Internet blind spot that breaks monitoring.
Autonomous IT requires visibility into the full delivery path, and most platforms stop short of it.
The gap is in the Internet Stack: the networks, CDNs, DNS, BGP routes and third-party services that sit between your infrastructure and your users.
Without that data, AI can correlate what it sees inside your estate, but cannot explain or act on failures that originate outside it.
Edwin AI plus Catchpoint closes that gap, combining infrastructure, app and Internet telemetry in one platform so the system can tell “our issue” from “upstream issue” and act accordingly.
Teams that add Internet visibility cut investigation time, reduce alert noise and trust automation more, because the platform is finally operating on the full picture of how services are delivered.
When AI started appearing in every monitoring platform, the pitch made sense. Correlate faster. Reduce noise. Stop chasing alerts at 2am. Most teams bought into it, and most were disappointed—not because the AI was bad, but because it lacked the full context needed to make correct decisions.
The Internet exists. Your tools mostly ignore it
A customer in Paris tries to check out on your site. Pages load, then stall. Your dashboards are green. Application metrics look normal. API health checks pass. Forty minutes later, someone traces it back to a regional ISP routing issue that never showed up in your internal tools. The problem was on the Internet, the road between the user and your front road that nobody was watching.
Why the Internet Stack is more complex than it looks
The Internet is now in the critical path of every digital service, and yet noted cybersecurity journalist Brian Krebs once observed that it’s held together with spit and bailing wire. He’s not wrong. A single user transaction might travel across a wireless network, through a CDN, hit a third-party API, resolve a DNS query, and pass through multiple routing hops before it reaches your application, and then do it all in reverse. Any one of those components can degrade or fail.
Each layer of the Internet Stack introduces dependencies you do not control, but as far as your customers are concerned, are your problem to fix. ISP congestion, BGP route changes, CDN performance, DNS failures and third-party API latency all show up as degraded user experience. None of them sit inside your data center. Without visibility into those layers, you cannot reliably diagnose or fix the issues your customers feel first.
Why this matters for Autonomous IT
Most IT teams have done the hard work of getting visibility into their own infrastructure. But genuine Autonomous IT, where systems detect, decide and act without constant human direction, needs visibility into the full path between a user and the service they’re trying to reach. For most platforms, that path goes dark the moment it leaves your infrastructure and enters the Internet.
LogicMonitor already had Edwin AI: an intelligence agentic layer that analyzes telemetry across hybrid infrastructure, applications, and digital experience, understands how systems relate through a unified context graph, and prioritizes response based on business impact rather than raw technical severity. The problem was the same one facing every other platform. The data Edwin AI operated on stopped at the edge of the infrastructure it could see. Before Catchpoint, Edwin AI could see how your own infrastructure and applications behaved, but it had to treat the Internet as a black box.
That is why LogicMonitor acquired Catchpoint, the company that is credited with building the Internet Performance Monitoring category and has been a Gartner Magic Quadrant leader for DEM for two years running. Catchpoint was built to provide visibility into the whole delivery path of the Internet, including BGP route changes, CDN degradation, DNS failures, last-mile wireless, and upstream API latency.
By adding Catchpoint, LogicMonitor now operates the world’s largest observability network, with over 3,100 intelligent collectors across cloud, backbone, last-mile, wireless, BGP peers and enterprise environments. That network feeds Edwin AI the Internet layer telemetry it formerly did not have visibility to: the signals that explain why users in a specific region are experiencing failures while every internal system shows green.
What changes in practice
When Internet performance data feeds into the same platform as infrastructure and application telemetry, the operational impact is concrete:
Teams can answer “is it us or the Internet?” in seconds rather than hours with AI-driven correlation.
Root cause identification moves from manual triage to contextual correlation across logs, metrics, topology, and internet telemetry.
Early warnings from Internet Sonar, synthetic tests and real user monitoring surface degradation before it impacts revenue or customer experience.
Edwin AI can trigger automated workflows in response, reducing the manual effort involved in every incident that previously required someone to work through fragmented tools.
SAP uses Catchpoint to support monitoring across their global customer operations. Alerts arrive within seconds. Root cause is identified in three minutes. That speed materially reduces how long an issue stays unresolved and how much customer impact it creates. Because application, infrastructure and Internet telemetry are analyzed together, you can detect and explain issues that originate in the network path instead of guessing.
Verizon relies on Catchpoint to proactively monitor critical customer journeys, delivering results including 4x faster problem identification and a 90% reduction in false alerts. Internet-layer insight lets them see degradation in user journeys that would otherwise be masked by healthy internal metrics.
Internet-stack visibility cuts investigation time, trims alert noise, and makes teams more confident that the platform will spot issues before customers do.
One platform, not two tools connected
The distinction worth making is that this is not a case of plugging an Internet monitoring tool into an existing observability stack via an integration. Catchpoint is part of the same data foundation Edwin AI runs on, sharing a single telemetry pipeline, a single context graph, and a single intelligence layer.
That matters because autonomy needs context, and real context only comes from connected data. If Internet performance signals live in a separate tool with a separate alert queue, the AI layer in your primary platform cannot act on them. The blind spots persist even if you technically have coverage. That is still how many monitoring stacks are stitched together, which is why their AI features tend to stop at better dashboards and smarter alerts instead of end‑to‑end action.
You cannot automate across a gap in your data.
User-to-code visibility, from the real user experience across the Internet and into the infrastructure and applications underneath, is a prerequisite for Autonomous IT to function as described rather than as a concept.
AI is already taking on more of the detection, correlation and response work in complex environments. The limiting factor now is not the models, but whether the AI layer sees the full context across the delivery path or only the part inside your walls. Catchpoint’s Internet performance monitoring, combined with Edwin AI’s context-driven intelligence and closed-loop orchestration capabilities, extends LogicMonitor’s visibility to the Internet edge, filling in the layer that has historically been outside the scope of infrastructure monitoring. That closes the data gap that has limited most “AI-powered” operations tools. Without that context, autonomy stays theoretical. With it, systems can start to act on the full truth of how services are delivered, not just the portion you directly run.
