Computer-using agent (CUA) models: Redefining digital task automation

As artificial intelligence evolves, its ability to interact with digital environments is reaching new levels of sophistication. Traditional automation tools rely on scripts and APIs to perform tasks, limiting their flexibility across different platforms. However, a new approach—Computer-Using Agent (CUA)—enables AI to navigate graphical user interfaces like humans, executing tasks through direct interaction with on-screen elements such as buttons, text fields, and menus.

Developed by OpenAI, CUA models integrate multimodal AI, reinforcement learning, and advanced reasoning to process visual inputs, understand contextual information, and execute actions dynamically. This allows them to automate complex workflows without requiring predefined rules or platform-specific integrations. By interpreting raw pixel data, CUA can work across various operating systems and web applications, making them a highly adaptable solution for digital task automation.

This article provides an in-depth exploration of CUA models. It examines the core technologies involved, operational principles, performance benchmarks, potential applications, real-world impact and more.

What are CUA models?

CUA models, or Computer-Using Agent models, mark a major breakthrough in the field of artificial intelligence, which is designed to interact with graphical user interfaces like humans. They can navigate buttons, menus, and text fields on a screen to complete various digital tasks. By combining GPT-4o’s vision capabilities with advanced reasoning through reinforcement learning, CUA operates without relying on OS- or web-specific APIs, making them highly adaptable across different interfaces.

Developed by OpenAI, CUA builds on years of research at the intersection of multimodal understanding and reasoning. By integrating advanced GUI perception with structured problem-solving, it can break down tasks into multi-step plans and adjust its approach when encountering challenges. This advancement enables AI to interact with the same tools humans use daily, expanding its potential applications.

How do CUA models work?

CUA processes visual input to understand and interact with digital environments, similar to how a human navigates a computer. Unlike traditional automation tools that rely on predefined scripts or platform-specific APIs, CUA interprets raw pixel data, making it adaptable to various interfaces and workflows.

Its operation follows a structured cycle of perception, reasoning, and action:

• Perception: CUA captures screenshots of the computer screen to analyze the current state of the digital environment. These images provide context for decision-making, allowing the system to recognize UI elements like buttons, text fields, and menus.

• Reasoning: Using chain-of-thought reasoning, CUA processes its observations, tracks progress across steps, and dynamically adapts to changes. By referencing both past and current screenshots, it refines its approach to problem-solving, ensuring accuracy even in complex workflows.

• Action: CUA executes tasks through a virtual mouse and keyboard, performing actions such as typing, clicking, and scrolling. For sensitive operations—like handling login credentials or solving CAPTCHA challenges—it requests user confirmation to maintain security.

By integrating these three components into an iterative loop, CUA efficiently completes multi-step processes, corrects errors, and adjusts to unforeseen interface changes. This makes it a versatile solution for automating tasks like filling out forms, navigating websites, and managing digital workflows without the need for custom API integrations.

Core tech components of CUA

Multimodal LLM

CUA utilizes a multimodal large language model, GPT-4o, that integrates text and vision capabilities. It processes and analyzes both textual and visual inputs, enabling these models to interact with complex digital environments that require understanding web layouts, images, and structured data. The combination of vision capabilities with advanced reasoning enhances the agent’s ability to interpret web pages, extract relevant information, and execute tasks with higher accuracy.

Natural Language Processing (NLP)

NLP is fundamental to computer-using agents, allowing them to understand, generate, and refine human-like text responses. Advanced NLP techniques ensure precise intent recognition, contextual understanding, and effective communication. This capability is critical when interacting with dynamic environments like WebArena, WebVoyager, and OSWorld, where CUA must process instructions, retrieve relevant content, and execute multi-step tasks based on natural language queries.

Reinforcement Learning (RL)

CUA leverages reinforcement learning to improve their decision-making and interaction strategies over time. In evaluation environments such as WebVoyager, RL enables agents to navigate real-world web pages efficiently, adapting to changes in content and structure. Through trial-and-error learning, these models optimize their performance, ensuring better task completion rates even in unstructured or evolving online environments.

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CUA performance evaluation: Key factors and methodologies

Several key factors influenced CUA’s performance, including the evaluation methodologies used. These evaluations were conducted in controlled environments with specific prompt designs, sampling parameters, and scoring procedures, all of which played a pivotal role in shaping the results.

1. Environments

The evaluation was conducted across multiple environments to assess the CUA’s performance in different operational settings. Notable environments included WebArena and WebVoyage, which are used to simulate web-based interactions and diverse online scenarios. Additionally, OSWorld was employed to test the system’s capabilities in a more controlled, offline, and system-level environment. By simulating these conditions, the results offered valuable insights into how the CUA performs across diverse contexts.

2. Prompts

Prompts used during the evaluation were carefully designed to simulate a broad range of real-world queries and tasks. The selection of prompts focused on diversity, ranging from simple questions to complex queries. This ensured a well-rounded assessment of the CUA’s ability to understand, process, and respond appropriately across varying levels of complexity.

3. Sampling parameters

The results of the CUA evaluations were obtained using autoregressive sampling. By default, the sampling process utilized a temperature setting of 0.6 and a maximum of 200 steps unless otherwise specified. These parameters were chosen to balance the generation quality and efficiency during the evaluation.

4. Scoring procedures

The scoring procedures measured the CUA’s performance across multiple metrics objectively. For WebVoyager, an automatic evaluation protocol powered by GPT-4 was utilized. Since WebVoyager simulates real websites, the content of these sites can change over time, which may lead to certain tasks becoming outdated or broken. As a result, the evaluation results may fluctuate over time. During the evaluation, 35 broken tasks were removed to ensure accurate scoring. These evaluations provided insights into the strengths and limitations of CUA models, guiding improvements in reasoning, adaptability, and task execution.

Performance benchmarks of computer-using agent models

CUA demonstrates notable advancements in executing both general computer tasks and browser-based operations. Its effectiveness is assessed through established benchmarks such as OSWorld, WebArena, and WebVoyager, which evaluate system interaction and web-based automation of AI agents.

Benchmark evaluations and results

1. OSWorld (Computer use benchmark): OSWorld provides a real-world computing environment for evaluating AI agents that perform tasks across multiple operating systems. It offers task setup, execution-based assessment, and interactive learning, allowing models to be tested in a realistic computing environment. This benchmark measures an agent’s ability to operate within fully functional operating systems, including Windows, macOS, and Ubuntu, by engaging with various software applications. CUA achieved a 38.1% success rate on OSWorld tasks, significantly outperforming the previous benchmark of 22.0%.

2. WebArena (Simulated browser tasks): WebArena is a controlled web environment designed to test the ability of autonomous agents to complete complex tasks on simulated websites. It includes four distinct website categories, structured to resemble real-world online platforms, and features embedded tools and knowledge sources for problem-solving. The benchmark assesses how well AI agents translate high-level natural language instructions into precise web interactions. WebArena also includes validation mechanisms that verify the functional correctness of task completion. CUA recorded a 58.1% success rate, exceeding the previous best performance of 36.2%. However, human performance on this benchmark stands at 78.2%, highlighting the complexity of web-based automation.

3. WebVoyager (Live web interaction): WebVoyager evaluates an agent’s ability to complete tasks on live websites such as Amazon, GitHub, and Google Maps. This benchmark measures real-time web interaction skills, including searching, navigating, and input handling. Since these tasks are structured and require accurate visual interpretation, agents are assessed based on their ability to interact with dynamic web elements using standard input methods like keyboard and mouse controls. CUA achieved an 87% success rate, matching human performance in this category.

CUA’s approach of interpreting screen pixels and executing commands via a virtual mouse and keyboard makes it adaptable across multiple digital environments. While it performs exceptionally well in structured browser interactions, its performance in complex workflows like OSWorld and WebArena still lags behind human users, highlighting areas for further enhancement. These results underscore CUA’s capability as a general-purpose digital assistant, capable of bridging the gap between automated task execution and human-like adaptability.

Operator: A real-world example of CUA

Operator, OpenAI’s first AI agent, is built on the CUA framework. It enables users to communicate with websites and applications using natural language commands. For example, a user can instruct the Operator to “Book a flight to New York next week,” and the agent will navigate travel websites, find flights, and complete the booking process. Unlike traditional automation tools that rely on predefined integrations, the Operator processes visual information from a screen, identifies interactive elements, and performs actions dynamically. This flexibility makes it a powerful tool for handling tasks across a wide range of websites and applications.

Operator’s capabilities and applications

The Operator’s primary function is to execute user-directed tasks on a computer, enabling it to interact with everyday applications. It can browse the internet, fill out forms, book reservations, make purchases, and perform other web-based tasks under human supervision. Unlike conventional AI chatbots that primarily respond to text queries, the Operator can visually process and interact with software interfaces, making it a practical example of a CUA in action.

Model training and development

The Operator was trained using a combination of supervised learning and reinforcement learning. Supervised learning equipped it with the base level of perception and ability to interpret screens and interact with UI elements, while reinforcement learning provided the model with higher-level capabilities, including reasoning, error correction, decision-making and adaptation to unexpected events. Operator’s training involved diverse datasets. These included a set of publicly available data, primarily from industry-standard machine learning datasets and web crawls, as well as datasets created by human trainers demonstrating computer-based task completion.

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Safety in CUA models

As CUA gains the ability to take direct actions in a browser environment, new safety concerns emerge. To address these risks, extensive testing and safeguards have been implemented across multiple layers, focusing on three key areas: misuse prevention, model accuracy, and resilience against adversarial threats. These measures apply at the model level, within the deployment system, and through ongoing monitoring to ensure safe operation.

Preventing misuse

To minimize the risk of harmful or unethical use, several controls are in place:

• Refusals: CUA is designed to reject harmful requests or illegal tasks.

• Restricted access: Certain websites, including those related to gambling, adult content, and regulated substances, are blocked from interaction.

• Real-time moderation: Automated safety checkers continuously assess user interactions to detect and prevent policy violations, issuing warnings or restrictions as needed.

• Post-use audits: A combination of automated detection and human review ensures that policy violations, including deceptive activities and child safety concerns, are swiftly addressed.

Minimizing model mistakes

The second risk category involves model errors, where the CUA unintentionally performs an action the user did not intend, potentially causing harm. These errors can range from minor (e.g., a typo) to severe (e.g., deleting a critical document). CUA is implemented with the following safeguards to minimize this risk:

• User confirmation: CUA requests user approval before executing actions with external consequences (e.g., submitting orders, sending emails, form submissions), ensuring human oversight.

• Restricted tasks: The model currently refuses to assist with high-risk tasks, such as banking transactions and decision-making in sensitive matters.

• Supervised mode: For sensitive websites (e.g., email), CUA operates in “watch mode,” requiring active user supervision for immediate error correction.

Defending against adversarial manipulation

Computer-using agent is designed to recognize and resist attempts to manipulate their behavior through prompt injections, jailbreaks, and phishing techniques. The safeguards implemented to counter this include:

• Cautious navigation: The model detects and ignores most adversarial prompts, including prompt injections on websites.

• Active monitoring: A secondary model incorporated in the Operator observes interactions and halts execution if suspicious content appears on the screen.

• Rapid response pipeline: Automated detection, combined with human review, flags suspicious behavior and enforces necessary restrictions.

Ongoing risk assessment

CUA also underwent evaluations aligned with broader AI safety frameworks, ensuring they do not introduce new risks beyond those identified in existing large-scale models like GPT-4o. These evaluations include autonomous replication testing and safeguards against biosecurity risks.

Given the evolving nature of AI capabilities and risks, CUA safety measures will continue to be refined based on real-world feedback and emerging challenges.

Potential applications of CUA models

CUA has broad applications across industries where digital tasks require intelligent automation without the need for custom integrations or API dependencies. By interacting directly with GUIs, they offer a flexible and scalable solution for streamlining workflows across different platforms.

1. Enterprise process automation

CUA models can assist in automating repetitive tasks such as data entry, document processing, and software configuration. Unlike traditional RPA solutions, they do not require predefined workflows and can adapt dynamically to changing interfaces. Some of the processes CUA can potentially automate include:

• Automating invoice processing and financial reconciliations

• Extracting and summarizing reports from enterprise dashboards

• Managing software installations and system updates across IT environments

2. Customer support and IT assistance

Computer-using agents can serve as virtual IT assistants, handling software troubleshooting, ticket management, and user support by navigating service portals and knowledge bases. It can potentially automate:

• Diagnosing and resolving common software issues

• Assisting users with password resets and account recovery

• Handling routine IT requests, such as software provisioning and permissions management

3. E-commerce and web interaction

By operating within live web environments, CUA can execute complex browsing tasks, making them useful for price monitoring, competitor analysis, and automated purchasing. The following are some of the tasks it can streamline:

• Automating product comparison and price tracking across multiple e-commerce platforms

• Filling out online forms and managing inventory updates

• Monitoring customer feedback and sentiment analysis from online reviews

4. Financial and legal compliance

CUA can assist professionals in navigating regulatory frameworks by extracting and verifying critical information from financial statements, contracts, and compliance documents. CUA models can:

• Review legal documents for compliance checks

• Automate financial data reconciliation and auditing

• Generate structured summaries from large regulatory filings

5. Healthcare and medical documentation

In healthcare, these models can enhance administrative efficiency by automating medical record management and patient data retrieval. It can potentially achieve the following tasks in healthcare:

• Assisting in electronic health record (EHR) data entry and retrieval

• Extracting key information from medical research and clinical trial documents

• Automating appointment scheduling and insurance verification processes

6. Education and research

CUA models can streamline research workflows by interacting with academic databases, summarizing articles, and managing citations. It can potentially execute the following:

• Automating literature reviews by summarizing research papers

• Assisting students and educators with digital learning platforms

• Extracting and organizing data from online courses and academic resources

By leveraging CUA in these domains, businesses can achieve greater operational efficiency, reduce manual effort, and improve accuracy in digital interactions. As CUA continues to evolve, its applications will expand further, bridging the gap between human cognition and AI-driven task execution.

Final thoughts

CUA models represent a major advancement in AI-driven automation by enabling intelligent interaction with graphical user interfaces. Unlike traditional automation tools that rely on predefined scripts or platform-specific APIs, these models interpret raw visual input, making them highly adaptable across different digital environments. Their ability to navigate interfaces, process information, and execute tasks using virtual keyboard and mouse controls allows them to function as versatile digital assistants in enterprise workflows, customer support, financial analysis, healthcare documentation, and more.

As organizations increasingly adopt computer-using agents for process automation and task execution, their role in bridging the gap between human-like interaction and AI-driven efficiency will continue to expand. Future advancements will likely focus on refining decision-making, improving contextual understanding, and enhancing security measures to ensure seamless and reliable integration into business operations.

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