Available in preview via the API, our Computer Use model is a specialized model built on Gemini 2.5 Pro’s capabilities to power agents that can interact with user interfaces. Read More
The iterative loop (screenshot → model → action → new screenshot) introduces increasing error risks that the article underestimates. In my testing of similar vision-based automation, each step depends on accurately interpreting the previous state. Misclicks can accumulate—if the model clicks the wrong element, the new screenshot shows that error, and subsequent actions are based on this mistake. By step 5-6, the agent is often completely off-track. The safety controls (per-step service, system instructions, user confirmation) seem thorough but, from testing similar setups, tend to conflict with automation autonomy. Asking for user confirmation for “high-stakes actions” undermines the automation’s value. What counts as high-stakes is subjective—does clicking “delete” qualify? What about “submit payment” with pre-approved amounts?
The demo videos conveniently skip these confirmation prompts. The benchmark performance comparison lacks detailed error analysis. Achieving over 70% accuracy still means around 30% task failure, which adds up during multi-step workflows. The pet spa demo shown at 3X speed hides actual execution time and likely showcases a cherry-picked success. Testing UI automation across my multi-system lab shows that demo-quality performance rarely translates well to production environments with different screen resolutions, loading times, and UI frameworks.
BC
October 8, 2025The iterative loop (screenshot → model → action → new screenshot) introduces increasing error risks that the article underestimates. In my testing of similar vision-based automation, each step depends on accurately interpreting the previous state. Misclicks can accumulate—if the model clicks the wrong element, the new screenshot shows that error, and subsequent actions are based on this mistake. By step 5-6, the agent is often completely off-track. The safety controls (per-step service, system instructions, user confirmation) seem thorough but, from testing similar setups, tend to conflict with automation autonomy. Asking for user confirmation for “high-stakes actions” undermines the automation’s value. What counts as high-stakes is subjective—does clicking “delete” qualify? What about “submit payment” with pre-approved amounts?
The demo videos conveniently skip these confirmation prompts. The benchmark performance comparison lacks detailed error analysis. Achieving over 70% accuracy still means around 30% task failure, which adds up during multi-step workflows. The pet spa demo shown at 3X speed hides actual execution time and likely showcases a cherry-picked success. Testing UI automation across my multi-system lab shows that demo-quality performance rarely translates well to production environments with different screen resolutions, loading times, and UI frameworks.