New Relic lifts the lid on the ‘black box’ of ChatGPT apps with dedicated observability offering
New Relic has launched a new observability solution aimed squarely at businesses building applications that run inside ChatGPT, addressing one of the biggest pain points in the fast-emerging ecosystem of AI-embedded apps: lack of visibility.
As more companies push their products and services directly into ChatGPT conversations—turning the interface into a new sales and engagement channel—engineering teams have struggled with what happens after deployment. Once an app is instantiated inside ChatGPT, it often disappears into a “black box”, where traditional browser monitoring tools fail to capture performance issues, user behaviour, or broken interfaces.
New Relic’s new monitoring capability is designed to close that gap. The company says it now provides end-to-end visibility into the performance, reliability, and user experience of custom ChatGPT apps, allowing developers to detect problems early, optimise conversion journeys, and confidently scale revenue-generating use cases built on generative AI.
Brian Emerson, Chief Product Officer at New Relic, said embedding services directly into ChatGPT conversations opens up a powerful new distribution and monetisation channel—but only if teams can see what is actually happening once the app is live. Without observability, he noted, businesses risk flying blind on user experience and system health.
Tackling the i-frame blind spot
The technical challenge lies in how ChatGPT renders third-party apps. Many are delivered inside restricted i-frame environments, governed by tight security headers, content security policies, and sandbox rules. In these conditions, standard monitoring tools often cannot detect layout shifts, broken buttons, failed scripts, or subtle user experience issues that cause drop-offs.
The problem is compounded by AI-generated interfaces. Applications can appear visually correct while failing functionally, AI-generated text can break carefully designed layouts, and so-called “ghost citations” can surface—where ChatGPT references data that the application backend never actually produced. Without deep telemetry, these inconsistencies can go unnoticed.
Full-stack visibility inside ChatGPT
New Relic says its ChatGPT app monitoring brings traditional observability techniques into this AI-hosted environment. Its browser agent collects granular telemetry from within the GPT i-frame, tracking latency, connectivity, script errors, console logs, and layout instability as AI responses stream in.
Crucially, the platform also maps user interactions—such as successful clicks, abandoned flows, or non-engagement—back to backend services, giving teams a full transaction trace from ChatGPT prompt to application response. Developers can define custom benchmarks and events, for example measuring whether an AI-generated chart rendered correctly or correlating “AI render success” with user bounce rates.
The new solution includes capabilities such as rage-click and dead-click detection to identify user frustration, monitoring of cumulative layout shift (CLS) inside i-frames, cross-origin performance insights, and end-to-end traceability across distributed systems.
As generative AI platforms like ChatGPT increasingly become front doors for digital services, New Relic’s move signals a broader shift in observability—one that recognises AI-hosted applications as first-class production environments rather than experimental side projects.