AI agents that can only read text output are flying blind. They execute a command but can't see what happened on screen. Agent Vision closes this loop: capture a screenshot, analyze the visual state, decide what to do, act, then capture again to verify. This scan-act-rescan cycle gives AI agents the same visual feedback humans rely on.
Your AI agent runs a command and gets text output — maybe an exit code, maybe some logs. But it can't see the actual result on screen. Did the button change color? Did the dialog appear? Did the layout shift? The agent is guessing. It can't course-correct because it can't observe the effect of its own actions.
After every action, the agent re-captures the screen. It can compare before and after screenshots, verify that expected elements appeared or disappeared, detect error states visually, and decide its next action based on what it actually sees. The feedback loop is tight: act, observe, adjust. Just like a human.
Capture baseline state
Take a screenshot before acting. The tag helps your agent track which capture is which.
Perform an action
Click, type, scroll — whatever the next step requires.
Re-capture after the action
Screenshot the result. Your agent compares this with the baseline to verify the change.
Discover new elements that appeared
After a UI change, new elements may appear (modals, error messages, new screens). Discover them.
Loop until the goal is reached
Each iteration brings the agent closer to its goal. The visual feedback tells it when to stop.
Requires macOS 13+ · No dependencies · ~4MB
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