New Relic wants companies to stop “flying blind” when integrating their services into ChatGPT. The company, specialized in observability and application monitoring, has announced a new capability designed for apps running within the conversational interface, aiming to provide visibility into performance, reliability, and user experience in an environment where — according to the provider — traditional tools often fall short.
The proposal comes at a time when more and more engineering teams are exploring ChatGPT as a channel for acquisition and conversion: not just to answer questions, but to guide users toward specific actions (purchase, booking, requesting a demo, or completing a flow). The issue, as New Relic argues, is that when an application “lives” inside ChatGPT, it can enter a kind of blind spot: an embedded experience within the conversation where the developer no longer controls the container nor has the same telemetry guarantees as in a “normal” browser.
The “Blind Spot” of i-frames and security restrictions
New Relic frames the technical challenge as a common pattern: applications rendered in an i-frame within the conversation. In these cases, the development team can lose critical signals needed to optimize UX and conversion: from unexpected layout shifts to clickable-looking buttons that don’t respond, or drop-offs without an obvious cause.
Adding to this “blind spot” are typical security layers: complex security headers, Content Security Policy (CSP), i-frame sandbox rules, and client storage limitations. Collectively, these restrictions can make standard frontend monitoring solutions struggle to collect reliable or comparable data.
Furthermore, New Relic highlights a specific nuance related to experiences generated or mediated by Artificial Intelligence: the final interface can be affected by “programmatically strange” behaviors, such as UI elements that appear correct but fail, generated texts that break a carefully designed CSS layout, or references that the AI presents as citations even though the backend didn’t serve that data.
From “Classic” Observability to Metrics for AI-Enhanced Experiences
The core message is straightforward: if ChatGPT becomes a new showcase, experience failures are no longer just technical incidents—they become friction points in the sales funnel. That’s why New Relic emphasizes the need to detect and measure issues precisely before fixing them (for example, a “hallucination” in the interface).
In their announcement, New Relic states that their browser agent is equipped to capture relevant telemetry even in these embedded contexts. Key signals include: latency and connectivity within the i-frame, script failures or syntax errors triggered by dynamic responses, and events logged in the browser console.
The innovation, however, isn’t limited to “performance.” New Relic’s focus is on how users interact with the app inside ChatGPT and suggests instrumenting “value actions” (such as clicking “buy now,” completing a form, or finishing a critical step). Based on this data, teams can build dashboards linking rendering quality with bounce or conversion rates, and track metrics designed for these experiences, such as an AI Render Success Rate or prompt-to-action metrics.
What exactly is measured: frustration, visual stability, and end-to-end traceability
The package is part of New Relic’s Intelligent Observability platform and is structured around four key functional blocks:
- Detecting user frustration: signals such as rage clicks, clicks on error-generating elements, or “dead” clicks help identify points where users try to progress but the interface does not respond as expected.
- Monitoring visual instability: the focus is on Cumulative Layout Shift (CLS) within the i-frame, a crucial metric for visual stability. In scenarios where content is “injected” or “streamed,” unexpected layout changes can trigger frustration and interaction errors (e.g., clicking in the wrong place).
- Cross-origin insights: visibility into behavior when the application does not control the top-level window, common in embedded experiences.
- End-to-end traceability: connecting user interactions within the i-frame to backend services to reconstruct the full transaction journey.
The approach encourages a shift in mindset: measure not just “load speed,” but whether the experience behaves as expected when AI layers are involved in rendering, text, or final composition.
Availability and first steps
According to New Relic, monitoring capabilities for apps within ChatGPT are already available on their platform. To get started, they recommend a typical adoption flow: install the latest version of the browser agent, define value actions (key interactions), and then set up custom events to analyze results and build dashboards.
The underlying message is that New Relic is positioning itself in an emerging space: observability of “third-party hosted” experiences (in this case, within ChatGPT), where UX depends both on the app’s code and the container’s policies, security protocols, and content presentation methods.
Frequently Asked Questions
What does “monitoring for apps within ChatGPT” mean?
It refers to instrumenting and measuring the performance and user experience of applications embedded in ChatGPT (e.g., inside an i-frame), where the developer does not control the main container.
Why does an i-frame complicate traditional monitoring?
Because the app does not “own” the top-level window and may be subject to security policies (CSP, sandbox) and limitations on storage/telemetry, which reduce visibility into errors, interactions, and UX metrics.
What is CLS and why does it matter in AI-generated content experiences?
CLS (Cumulative Layout Shift) quantifies visual stability by measuring how often unexpected content shifts occur. When content is injected dynamically, layout changes can cause frustration and misclicks.
What should a company measure if it wants to sell within ChatGPT?
Beyond latency and errors, it’s useful to define “value actions” (key clicks, form completions, conversions) and relate them to signals of rendering quality, visual stability, and friction points (dead clicks, errors, drop-offs).