Benchmarking a Computer Use Agent（CUA） across applications starts with defining measurable tasks and consistent evaluation criteria. Because CUAs operate visually, benchmarks should include representative workflows such as opening menus, filling forms, navigating dashboards, exporting files, or completing end-to-end tasks like data uploads. For each workflow, you can measure success rate, completion time, misclick frequency, OCR accuracy, and resilience to layout changes. These metrics help developers understand how well the CUA generalizes across different applications and workflows.
To ensure comparability, each benchmark scenario should run multiple times under controlled conditions: identical resolutions, consistent window placement, and stable network conditions. Developers often create sandboxed environments or virtual desktops to eliminate external noise such as notification pop-ups or system-level interruptions. During each run, the CUA should record screenshots, action logs, and detection confidence scores. This data allows you to analyze why failures occur—for example, blurry text, button ambiguity, or slow-loaded dialogs.
Developers can enhance benchmarking by storing embeddings of screen states and results in a vector database such as Milvus or Zilliz Cloud. By retrieving similar states across different applications, it becomes easier to analyze patterns, cluster failure cases, and identify UI types that consistently challenge the agent. For example, if multiple applications use similar modal dialogs and the CUA struggles with these dialogs, similarity search will reveal this trend quickly. This embedding-based analysis helps refine both the CUA’s vision model and its action policies, making the benchmarking process more actionable and data-driven.