The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use (2411.10323v1)

Abstract: The recently released model, Claude 3.5 Computer Use, stands out as the first frontier AI model to offer computer use in public beta as a graphical user interface (GUI) agent. As an early beta, its capability in the real-world complex environment remains unknown. In this case study to explore Claude 3.5 Computer Use, we curate and organize a collection of carefully designed tasks spanning a variety of domains and software. Observations from these cases demonstrate Claude 3.5 Computer Use's unprecedented ability in end-to-end language to desktop actions. Along with this study, we provide an out-of-the-box agent framework for deploying API-based GUI automation models with easy implementation. Our case studies aim to showcase a groundwork of capabilities and limitations of Claude 3.5 Computer Use with detailed analyses and bring to the fore questions about planning, action, and critic, which must be considered for future improvement. We hope this preliminary exploration will inspire future research into the GUI agent community. All the test cases in the paper can be tried through the project: https://github.com/showlab/computer_use_ootb.

• The paper demonstrates that Claude 3.5 offers an integrated API solution for end-to-end GUI automation across web, productivity, and gaming tasks.
• It evaluates performance on planning, action, and critic dimensions, showcasing both successful executions and notable errors in complex operations.
• The study provides actionable insights and benchmarks to guide future improvements in error detection and training fidelity for GUI-based agents.

Comprehensive Case Study of GUI Automation with Claude 3.5 Computer Use

This paper presents a detailed preliminary case paper exploring the capabilities and limitations of Claude 3.5 Computer Use, an API-based GUI automation model recently released by Anthropic. The paper emphasizes its performance in various desktop task automation domains, including web navigation, workflow coordination, office productivity, and video game interaction. This evaluation not only provides insights into the current capabilities of Claude 3.5 but also identifies critical areas for improvement.

Core Findings and Structure

The authors propose that Claude 3.5 marks a significant advancement in GUI-based agents as it provides an end-to-end API solution for task automation without relying on additional external knowledge or GUI parsing. To evaluate this potential, the paper investigates multiple domains, recognizing the complexity inherent in tasks that require effective planning, precise action execution, and robust critic capabilities.

Evaluation Metrics

Three critical dimensions are defined for task performance assessment:

• Planning: This involves generating a viable execution plan derived from user queries. The plan must detail correct task flows and executable steps.
• Action: The agent's capacity to accurately identify and engage with GUI elements, executing a given plan through step-by-step interaction.
• Critic: The model's ability to monitor ongoing tasks, adapting to environmental changes by retrying or stopping actions appropriately once a task is completed.

Numerical Results and Bold Claims

The case studies cover 20 tasks across a range of desktop applications and present varying outcomes. Successful completion is documented in domains like web searches for products, executing workflow tasks across applications, and office productivity tasks like email management and document formatting. However, failures are also acknowledged, highlighting instances where the model's planning or execution was inadequate, such as failing to apply precise text formatting in productivity software, or when incorrect default actions were taken in web navigation.

Implications for Future Research

The paper acknowledges the challenge present in GUI automation where the intricacy of human-like interaction poses a significant obstacle. For instance, while the model successfully navigates through complex interfaces in specific games and productivity applications, it struggles with tasks requiring high precision, such as detailed text editing and consistent visual recognition under varying conditions.

The paper suggests several future directions:

• Benchmark Development: Enhanced and diverse benchmarking environments are necessary for more exhaustive evaluations of GUI models.
• Error Reduction: Improving the self-monitoring and error-detection mechanisms of the model is critical to minimize planning and critic errors.
• Training Fidelity: Current limitations in training data may not fully capture human interactions, highlighting a need for more comprehensive and context-enriched datasets.

Conclusion

The authors conclude that the Claude 3.5 model provides foundational capabilities that make significant strides in GUI automation but also clearly demonstrate areas for future research. By introducing the Computer Use Out-of-the-Box framework, the paper offers the community an accessible platform for extended exploration and benchmarking of GUI automation models, contributing crucial insights to the ongoing development in AI-driven computer use. The paper serves as a pivotal resource for researchers aiming to enhance the operational scope and reliability of future GUI-based autonomous agents.