See the Real AI Experience with Cato DEM

AI is becoming part of everyday work. Employees rely on services such as ChatGPT and Claude to research, create, and solve problems, while developers increasingly use AI-powered tools like Cursor and coding agents in their development workflows.

As employees increasingly rely on AI to work more productively, the AI experience is becoming a critical part of the digital experience IT teams are expected to support. Slow responses, failed interactions, or inconsistent performance can disrupt workflows and undermine the productivity gains AI is meant to deliver.

The challenge is that IT may have limited visibility into the AI interaction itself. Teams may not know there is a problem until employees start complaining.

Cato Digital Experience Monitoring (DEM) addresses this visibility gap with AI Experience Monitoring, providing IT teams with live visibility into AI latency and failures, along with the context they need to investigate performance problems.

AI Is Becoming Critical to Everyday Work. Can IT See When It Fails?

As AI becomes embedded in daily workflows, its performance increasingly affects employee productivity. An employee waiting on ChatGPT or Claude, or a developer relying on an AI coding tool, expects that interaction to work quickly and reliably.

That creates a new digital experience for IT teams to support.

But knowing that an AI application is reachable does not necessarily tell IT whether the actual AI interaction is performing well. Teams may only learn about slow responses or failed interactions when a user opens a ticket saying, “Claude is slow today.”

Then the investigation begins. Is the issue associated with a particular AI service or model provider? Is it concentrated at a specific location? Does it affect users accessing the service in a particular way?

The challenge for IT is no longer simply whether employees can reach an AI service. It is knowing whether the AI experience is working well, before employees must tell them it isn’t.

Traditional Application Monitoring Can Obscure the Real AI Experience

Monitoring an AI application is not the same as monitoring an employee’s interaction with the AI model.

GenAI applications such as ChatGPT, Claude, and Cursor generate many different types of network traffic. The agentic portion of that traffic, including conversational interactions with AI models and traffic from coding agents, can be mixed with software downloads and updates, authentication requests, licensing and configuration calls, telemetry, and other background activity.

Measuring all this traffic together can create a noisy representation of the actual AI experience. For example, a large application download may affect usage figures even though it has nothing to do with an employee’s conversation with an AI model. Performance measurements can similarly mix model-provider latency with unrelated application activity.

As a result, IT can see application traffic without getting clean answers to two fundamental questions: How quickly is the AI responding, and how often are AI interactions failing?

To understand the real AI experience, IT needs to separate this AI traffic from the surrounding application noise.

Isolate the AI Traffic That Matters with Cato DEM

Cato DEM addresses this challenge with AI Agent Experience Monitoring.

Instead of treating all traffic generated by a GenAI application as a single experience, Cato isolates the AI portion of that traffic, the interactions with AI model providers, including conversational traffic and traffic from coding agents such as Claude Code. By separating these AI interactions from downloads, updates, authentication, telemetry, and other background traffic, Cato gives IT a clearer view of the experience that matters to users.

The capability focuses on two fundamental indicators: latency, showing how quickly the AI service is responding, and errors, showing how often AI interactions are failing.

IT can examine performance by application and model-provider domain to identify where degraded experiences are occurring. At launch, coverage includes ChatGPT, Claude, Cursor, and the OpenAI and Anthropic APIs, with additional AI services to be added over time.

And because AI Agent Experience Monitoring is built directly into Cato DEM, AI performance becomes part of the same digital experience monitoring environment IT teams already use.

Instead of waiting for someone to report that “AI is slow,” IT gains live visibility into the AI traffic itself.

From “AI Is Slow” to Knowing Where to Investigate

Detecting poor performance is only the first step. IT also needs context to narrow the investigation.

Cato provides a dedicated AI Agent Experience Monitoring drill-down, where teams can analyze latency and errors by model provider, domain, and site. They can also see how employees are accessing the AI service.

Cato DEM also shows how users access AI services, whether through a browser, terminal, script, or coding tool. For example, IT can distinguish between someone accessing Claude through a browser and a developer using a coding tool such as Claude Code.

Consider that complaint: “Claude is really slow today.”

Without AI-specific experience visibility, IT starts with a broad application-level investigation where model interactions are mixed with other Claude traffic. With Cato DEM, teams can focus on latency and failures in the actual AI traffic and then use provider, site, and client context to narrow the investigation.

This turns a vague user complaint into a much more focused performance investigation.

Cato DEM measures the network experience of that traffic; it does not monitor what an AI agent does.

Catch Poor AI Experiences Before They Become User Complaints

AI is becoming another digital tool employees rely on to get work done. Cato DEM gives IT teams the visibility to support that experience proactively.

Teams can identify slow AI interactions without background application traffic obscuring the picture. They can spot failed interactions through error monitoring. And when something goes wrong, the provider, site, and access context give IT operations and network teams a more focused starting point for troubleshooting.

Most importantly, this visibility lives within DEM alongside the performance data that the performance data teams already monitor. IT can extend the same operational approach used to support other employees’ digital experiences to AI services, making them part of everyday work.

As AI becomes increasingly important to employee productivity, waiting for users to report poor performance is no longer enough. Cato AI Agent Experience Monitoring helps IT see the real AI experience, so teams can find slow or failing AI before users must tell them something is wrong.

For detailed configuration guidance, refer to the Cato Knowledge.