How AI Vision Agents Convert LLM Screenshot Coordinates Into Browser Clicks

Modern AI browser agents are moving beyond text-based DOM selectors toward vision-driven systems that interpret screenshots to interact with web pages. When a large language model analyzes a screenshot, it outputs normalized coordinates—such as 52% across and 38% down—that must be translated into precise browser pointer events. This translation requires a multi-step pipeline covering denormalization, viewport scaling, DOM hit-testing, and action dispatch. The process is complicated by factors like device pixel ratios, image downsampling for token limits, and dynamic or obfuscated web elements that defeat traditional CSS selectors. Engineers building these production-grade agents must also account for spatial reasoning errors, including quantization drift and aspect ratio distortion introduced by stochastic visual inference.