OpenAI’s new Operator agent has been touted as a game-changer for automating web tasks. It’s a semi-autonomous AI that can “operate” a web browser much like a human, clicking links, filling forms, and navigating sites on the user’s behalf. OpenAI’s CEO Sam Altman even called Operator “the beginning of our step into agents,” and President Greg Brockman proclaimed “2025 is the year of agents.” With such bold claims, one might expect Operator to revolutionize how we test web and mobile applications. After all, an AI that can mimic user interactions could theoretically execute end-to-end tests automatically.
However, after examining Operator’s capabilities through the lens of a QA professional, it’s clear that Operator falls flat when applied to real-world web and app testing. While the technology is impressive, its current form has significant limitations that make it impractical as a testing solution. In this post, we break down why Operator isn’t ready for prime time in QA and why we created GPT-Driver specifically to overcome these shortcomings.
What Operator Promises vs. What It Delivers
Operator is part of a research preview, available to ChatGPT Pro subscribers (at $200/month). It aims to demonstrate how an AI agent can handle online tasks end-to-end. For general users, this sounds exciting—imagine an AI agent ordering your groceries or booking tickets without you lifting a finger. Operator can handle simple tasks like making restaurant reservations via OpenTable or assembling shopping orders on Instacart. It achieves this by using a cloud-based browser that you watch in real-time, as it moves a cursor, clicks buttons, and types just like you would.
For software testing, these capabilities spark ideas. An AI that navigates a website could potentially run through test scenarios: clicking through signup flows, filling login forms, or simulating user actions on a web app. In theory, Operator could execute repetitive UI tests while the tester observes, stepping in only when needed.
The reality, unfortunately, is less promising. Early users and OpenAI itself have uncovered multiple gaps between Operator’s promise and performance, especially in a testing context. OpenAI has been transparent that Operator struggles with complex interfaces and unfamiliar workflows—exactly the kind of scenarios that thorough app testing would involve. In practice, using Operator for testing ends up requiring a lot of human intervention and doesn’t replace traditional test automation tools.
Key Limitations of Operator for QA Testing
Several inherent limitations make Operator ill-suited for robust web and app testing:
High Overhead and Confirmation Needs
Rather than a fire-and-forget test runner, Operator behaves more like a cautious digital assistant. It frequently pauses for user input or confirmation, especially for any action with consequences (e.g., clicking “Buy” or sending an email). While this is a smart safety feature for general use, in automated testing, it’s a deal-breaker. We need tests to run unattended. One reviewer noted that Operator, with its constant confirmations, “resembles an over-cautious assistant rather than a fully autonomous agent.” In other words, it still requires a human in the loop, negating much of the automation benefit for testing purposes.
Web-Only, Lacking Native App Support
Operator is designed for web interactions through a browser. It’s not built to test native mobile apps or handle mobile-specific gestures out of the box. While you can observe it on a mobile device (since it’s all in the cloud), it isn’t actually running an iOS or Android app—it’s operating a browser. Modern app testing always requires simulating native app usage or interacting with device features, which Operator simply doesn’t do. This is a critical gap if you’re looking at comprehensive mobile app QA.
Limited Remote Browser
Operator doesn’t run in your local browser or device at all—it runs on a remote browser hosted in OpenAI’s data centers. You watch its actions via a streamed interface. While this allows it to run from any device, the downside is huge: many websites detect and block automated agents. In fact, popular sites like Reddit already block AI-driven browsers, meaning Operator can’t even access them. OpenAI also deliberately restricts Operator from certain heavy or sensitive sites (like Figma or YouTube) in this preview phase. For QA testers, this walled-off environment means you cannot rely on Operator to test everything your real users would encounter. Any site with bot detection or not on OpenAI’s “allowed” list will stop Operator at the door.
Missing Tooling for Reliability
To perform reliable and scalable automated testing, tooling is required to optimize test prompts efficiently, evaluate LLM execution costs, and measure execution speed across different models. Additionally, effective tooling allows version control for test scripts and ensures consistency in repeated test executions. Without built-in support for these essential testing functions, Operator is not a viable choice for large-scale QA automation.
Lack of Control Over Key Testing Factors
A significant limitation for QA professionals is that Operator does not allow control over crucial testing parameters such as browser versions, language settings, or location data. This lack of configurability makes it impossible to test localized experiences, regional compliance requirements, or browser-specific behavior—elements that are fundamental to thorough software testing.
Why We Created GPT-Driver to Fill the Gap
All these limitations highlight a simple truth: general-purpose AI agents like Operator are not replacements for dedicated testing solutions. Operator is a fascinating proof-of-concept for letting AI loose on a web browser, but its “research preview” nature means it’s not production-ready for QA. Recognizing this gap, we took a different approach when building GPT-Driver.
We conceived GPT-Driver when GPT-3.5 was released, as we realized that LLMs and general AI tools like ChatGPT lacked the specialized tooling and integrations needed for seamless end-to-end testing. To deliver exceptional user experiences in vertical use cases like software testing, we needed a solution built specifically for automation, performance evaluation, and integration into testing pipelines. Our development of GPT-Driver predates Operator, though Operator’s launch has further reinforced the need for an AI-native testing agent tailored to vertical applications, rather than a general-purpose horizontal tool.
GPT-Driver focuses on the needs of QA teams: it can interpret test scenarios written in plain English, interact with both web and mobile apps more flexibly, and integrate into testing pipelines seamlessly. It also provides robust tooling for repetitive test execution, fine-tuning test prompts, evaluating LLM costs, optimizing execution speeds, and maintaining deterministic behavior in automated testing.
Conclusion: Promising Tech, Wrong Tool for Testing (For Now)
OpenAI’s Operator is an impressive step toward autonomous agents interacting with software the way humans do. Its ability to control a browser like a person opens doors to new possibilities in automation. But as we’ve seen, its current incarnation falls short for web and app testing needs. From environment limitations and intervention requirements to lack of testing focus, Operator simply isn’t ready to replace your Selenium or Appium tests just yet.
For QA professionals, the takeaway is to temper the excitement with reality. Operator is a preview, not made for QA, and using it for serious testing would introduce more headaches than it solves. The good news is that the industry is learning from these shortcomings. Tools like GPT-Driver have emerged specifically to address the void, offering testers a way to leverage AI without sacrificing reliability or coverage.
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