Computer-Using Agent

OpenAI pushes AI agent capabilities with new developer API

Developers using the Responses API can access the same models that power ChatGPT Search: GPT-4o search and GPT-4o mini search. These models can browse the web to answer questions and cite sources in their responses.

That’s notable because OpenAI says the added web search ability dramatically improves the factual accuracy of its AI models. On OpenAI’s SimpleQA benchmark, which aims to measure confabulation rate, GPT-4o search scored 90 percent, while GPT-4o mini search achieved 88 percent—both substantially outperforming the larger GPT-4.5 model without search, which scored 63 percent.

Despite these improvements, the technology still has significant limitations. Aside from issues with CUA properly navigating websites, the improved search capability doesn’t completely solve the problem of AI confabulations, with GPT-4o search still making factual mistakes 10 percent of the time.

Alongside the Responses API, OpenAI released the open source Agents SDK, providing developers with free tools to integrate models with internal systems, implement safeguards, and monitor agent activities. This toolkit follows OpenAI’s earlier release of Swarm, a framework for orchestrating multiple agents.

These are still early days in the AI agent field, and things will likely improve rapidly. However, at the moment, the AI agent movement remains vulnerable to unrealistic claims, as demonstrated earlier this week when users discovered that Chinese startup Butterfly Effect’s Manus AI agent platform failed to deliver on many of its promises, highlighting the persistent gap between promotional claims and practical functionality in this emerging technology category.

OpenAI launches Operator, an AI agent that can operate your computer

AI, AI computer use, chatgpt, ChatGPT Pro, computer use model, Computer-Using Agent, CUA, machine learning, openai, OpenAI operator / 9u50fv / January 23, 2025

While it’s working, Operator shows a miniature browser window of its actions.

However, the technology behind Operator is still relatively new and far from perfect. The model reportedly performs best at repetitive web tasks like creating shopping lists or playlists. It struggles more with unfamiliar interfaces like tables and calendars, and does poorly with complex text editing (with a 40 percent success rate), according to OpenAI’s internal testing data.

OpenAI reported the system achieved an 87 percent success rate on the WebVoyager benchmark, which tests live sites like Amazon and Google Maps. On WebArena, which uses offline test sites for training autonomous agents, Operator’s success rate dropped to 58.1 percent. For computer operating system tasks, CUA set an apparent record of 38.1 percent success on the OSWorld benchmark, surpassing previous models but still falling short of human performance at 72.4 percent.

With this imperfect research preview, OpenAI hopes to gather user feedback and refine the system’s capabilities. The company acknowledges CUA won’t perform reliably in all scenarios but plans to improve its reliability across a wider range of tasks through user testing.

Safety and privacy concerns

For any AI model that can see how you operate your computer and even control some aspects of it, privacy and safety are very important. OpenAI says it built multiple safety controls into Operator, requiring user confirmation before completing sensitive actions like sending emails or making purchases. Operator also has limits on what it can browse, set by OpenAI. It cannot access certain website categories, including gambling and adult content.

Traditionally, AI models based on large language model-style Transformer technology like Operator have been relatively easy to fool with jailbreaks and prompt injections.

To catch attempts at subverting Operator, which might hypothetically be embedded in websites that the AI model browses, OpenAI says it has implemented real-time moderation and detection systems. OpenAI reports the system recognized all but one case of prompt injection attempts during an early internal red-teaming session.

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