Daniel O'Shea monogram

OpenAI’s Computer Use Agent vs. Tracking Links: Overcoming a Key Limitation

AI Smart Summarization

Introduction

The rise of agentic AI means our models can now use computers on our behalf. OpenAI’s new Computer Use agent is one such system – it allows a GPT model to navigate a computer (especially the web) like a human, meaning it can open applications, click through websites, and even read on-screen content. One exciting use case is asking the agent to summarize online content for you, turning a lengthy article or email link into a neat summary. However, in experimenting with this agent, I discovered a critical limitation: it struggles with links that contain tracking redirects (the kind often found in email newsletters). In this post, I’ll share how I set up the Computer Use agent, where it stumbled, and the clever workaround that combined multiple tools – including an async browser interface and OpenAI’s latest GPT-4.1 model – to summarize content behind tracking links successfully. Finally, I’ll offer some suggestions for how OpenAI could improve this agent for everyone.

What is the OpenAI Computer Use Agent?

OpenAI’s Computer Use Agent (CUA) is essentially a GPT-4-based AI that can operate a computer by “seeing” the screen and interacting with user interfaces. In other words, it’s a tool-using AI that can control a web browser or other apps to accomplish tasks. The CUA model has vision capabilities (to interpret screenshots) combined with advanced reasoning to perform actions like clicking buttons, scrolling pages, or typing input. This universal interface approach lets the agent navigate websites and applications just as a person would – without needing specialized APIs for each site. For AI enthusiasts, this represents a step towards more general-purpose agents: you can give it an instruction in natural language, and it will figure out how to execute that on the screen. One obvious application of such a tool is web content summarization. Instead of manually opening a link and reading through a long article or email, you could ask the agent to do it: “Go to this URL and give me a summary of the main points.” The Computer Use agent is designed to handle exactly that kind of request by autonomously browsing the page, reading its content (via the model’s vision on screenshots or extracted text), and then providing a summary in plain English. In theory, it bridges the gap between the vast information on the web and the concise understanding that GPT can provide.

Quick Setup for Summarizing Content

Getting started with the Computer Use agent was surprisingly easy. OpenAI provides an Agents SDK (Software Development Kit) that makes it straightforward to create an agent equipped with tools like the computer-use capability. In my case, I followed an example that integrated OpenAI’s CUA model with a service called Browserbase (which provides cloud-based browsers for automation). With just a few steps – cloning a GitHub repository and plugging in some API keys – I had a working agent. I could literally run a one-line command to have the agent open a site and report information. For example, the sample app let me do:

python cli.py --computer browserbase --input "go to Hacker News, tell me the top news"

With that prompt, the agent would launch a browser, navigate to Hacker News, and then read the page to tell me what the top story was. This quick setup demonstrated the promise of the tool. In no time, I was able to ask for a summary of a website’s content and get a coherent answer back, all through the agent’s autonomous actions. It felt like having a smart assistant who could not only find information online but also synthesize it for me. Encouraged by these early results, I moved on to a slightly more real-world scenario: summarizing articles from links embedded in an email newsletter. Many of us receive daily or weekly emails with interesting links, and being able to quickly summarize those links with an AI agent could be a huge time-saver. I assumed the Computer Use agent would handle this just as easily as the Hacker News example – after all, a link is a link, right? Well, not quite.

The Tracking Link Limitation

Here’s where I hit a snag. The links in emails (like newsletters or marketing emails) are often not direct URLs to the content. Instead, they are tracking links – URLs that first send you to a tracking server (to log that you clicked) and then redirect your browser to the actual article. For a human using a normal browser, this redirect happens so fast you hardly notice; you end up at the article after a second. But for the AI agent, this added step proved troublesome. When I prompted the Computer Use agent with one of these email links, it didn’t yield the expected summary of the article. In fact, it struggled to get to the article at all. From what I could observe, the agent either got stuck on the tracking page or didn’t wait long enough to follow the redirect. Essentially, the intermediary URL confused the agent. Instead of seeing the content of the article, the agent might have been seeing a nearly blank page or a spinner – not something it could summarize. This limitation revealed an edge case for the Computer Use agent: it wasn’t reliably handling HTTP redirects, especially the kind triggered by tracking systems. In practical terms, this meant the agent as provided was a poor fit for summarizing links coming from emails or any source that uses redirect links. That’s a big drawback because a lot of interesting content is accessed via such links. If I forwarded an email to the agent or gave it a newsletter link, I got either an error or a useless result. The ease-of-use I enjoyed with direct links suddenly evaporated when a redirect was involved.

It’s important to note that this isn’t a bug per se, but rather a current limitation of the agent’s design. The Computer Use agent operates in a loop of sending actions (clicks, scrolls) and receiving observations (screenshots or page text). If one of those actions leads to a redirect, the agent needs to handle it – possibly by waiting, or by recognizing the redirect and continuing – and it seems the out-of-the-box agent wasn’t doing that in my case. So, I had a capable AI that could summarize direct web pages just fine, but it hit a wall with something as common as an email tracking link.

The Workaround: AsyncComputer + Browserbase + GPT-4.1

Being an AI enthusiast, I didn’t want to give up on the idea of automated summaries for all my links. So I looked for a workaround using the tools and customization options available. The solution I arrived at was to roll my own improved agent by combining a few powerful components:

  • AsyncComputer interface – a programming interface that allows the agent to perform computer actions asynchronously (in the background). The OpenAI Agents SDK lets developers implement this interface to take full control of how browser actions are executed. By creating a custom AsyncComputer implementation, I could ensure that when the agent clicked a tracking link, my code would truly navigate to the final destination (following any redirects) before handing back data to the model. In short, this interface gave me manual control to handle things like redirect delays properly.
  • OpenAI Agents SDK – the framework provided by OpenAI to orchestrate agents and tools. I used the Agents SDK to build a custom agent logic that uses my AsyncComputer implementation instead of the default. The SDK is designed to be flexible: “works great out of the box, but you can customize exactly what happens,” as OpenAI notes. This allowed me to plug in the modified computer-use tool and still leverage the rest of the agent loop (planning and reasoning) without starting from scratch.
  • Browserbase – a cloud-based browser automation platform that became the engine for actually loading web pages. Browserbase essentially provides headless browsers that I can control via an API. By integrating Browserbase with the AsyncComputer interface, whenever the agent needed to open a URL, it happened on a real browser in the cloud. Browserbase follows any redirect and loads the final page, then returns the page state (like a screenshot or the HTML text) back to the agent. Using it ensured that the agent saw the real content (as a human would) after the redirect.
  • GPT-4.1 – OpenAI’s latest GPT-4 model (released in 2025) to power the agent’s understanding and summarization. GPT-4.1 is an upgrade with improved reasoning and a larger context window, making it even better for complex tasks. By using GPT-4.1 as the brain of the agent, I benefited from its advanced ability to comprehend the page content (even lengthy articles) and produce a coherent summary. In fact, OpenAI specifically noted that GPT-4.1 models are more effective at powering agents for real-world tasks, thanks to these improvements.

With this combination, I effectively built a smarter web-summarizing agent. Here’s how the flow works now: The agent (powered by GPT-4.1) decides it needs to click a link to get the content. The AsyncComputer interface intercepts this action and uses Browserbase to load the page. Browserbase follows any redirect and loads the final page, then returns the page state (like a screenshot or the HTML text) back to the agent. GPT-4.1 then analyzes that actual page content and generates a summary. All of this happens in a seamless loop, so from the outside it just looks like the agent handled the link correctly.

The result? Success. I tested the same newsletter tracking links with this new setup, and the agent was able to navigate to the article and summarize it in detail. No more blank outputs or confusing errors – the tracking link was effectively neutralized by the fact that a real browser (via Browserbase) was in play. The agent could now summarize anything I threw at it, whether a direct URL or a redirect-laden email link.

This workaround showcases the power of combining tools: even when the base agent had a limitation, the flexibility of the Agents SDK and third-party tools allowed me to overcome it. It does require more engineering effort (and some extra cost for using an external browser service), so it’s not something a non-developer end-user could easily do. But it points the way toward how these agents can be extended and where they might go in the future as the ecosystem matures.

Recommendations for Improving the Computer Use Agent

While I’m happy I found a solution, it would be even better if the Computer Use agent handled such cases out of the box. Based on this experience, here are a few recommendations for OpenAI to enhance the agent’s usefulness, especially regarding tracking links and redirects:

  • Automatic Redirect Handling: The agent’s built-in browser navigation should detect when a page triggers a redirect (like an HTTP 302 or meta refresh) and automatically follow it to the final URL.
  • Timeout and Wait Logic: Often, a tracking link might briefly show a “redirecting…” message. The agent should have a smarter wait mechanism – recognizing that if a page is mostly empty or has certain keywords like “redirecting”, it should pause and allow the redirect, rather than immediately concluding or giving up.
  • Integrated Browser Environment: OpenAI could integrate a headless browsing environment on their side for the Computer Use tool. Instead of requiring developers to implement the AsyncComputer interface with a third-party solution, the agent’s actions could be executed in an OpenAI-managed browser that handles things like cookies, scripts, and redirects properly.
  • Documentation and Examples for Edge Cases: Improving the documentation to highlight how to handle cases like tracking links would be valuable. Providing an example in the Agents SDK docs for “navigating a redirecting link” using the SDK’s tools would spread awareness of the issue and its fixes.

Conclusion

The OpenAI Computer Use agent is a fascinating glimpse into the future of AI that can act on our behalf digitally. My journey with it went from initial excitement – seeing it complete tasks like summarizing a webpage with minimal setup – to the frustration of hitting a real-world limitation, and finally to the satisfaction of solving that problem with a bit of creativity and engineering.

The experience underlines a key point in today’s AI landscape: no single tool is perfect, but with an ecosystem of tools we can often fill the gaps. By using the OpenAI Agents SDK, an external browser service, and a powerful model like GPT-4.1 together, I achieved what I needed, showing the potential of modular AI design.

For AI enthusiasts and developers, the takeaway is that agentic AI is here, and still evolving. If you’re implementing something with the Computer Use agent (or similar agents), be prepared to handle edge cases like this. And for OpenAI and others building these systems, the challenge is to smooth out such edges so that the next wave of users can enjoy a frictionless experience. I’m optimistic - given how quickly this field moves - that improvements will come soon. In the meantime, I’ll be enjoying my AI-augmented web surfing, now that it can click through those pesky tracking links for me.