What it is, and why agents need it. Definitions, the reliability-compounding argument, and how the approaches compare.
Structured browser perception is the practice of giving an AI agent a machine-readable representation of a live rendered page — element identity, text, layout, state — instead of pixels or raw markup. The agent reasons over structure it can address, not an image it has to re-interpret or a markup dump it has to wade through.
Published evaluations of vision-based agents show per-action success rates around 80–85 percent on real desktop and web tasks. That sounds workable until the steps compound.
Exact benchmark citation added when verified; no vendor or model names on this page.
Condensed from Runtime Snapshots 16. Three ways to give an agent a page, compared on the dimensions that decide real-world use.
| Approach | Cost / page | Authenticated coverage | Element addressing | Drift |
|---|---|---|---|---|
| Screenshots + vision | High (vision tokens) | Yes | No | High |
| Static HTML / DOM extraction | Low | Partial | Yes (noisy) | Medium |
| Runtime structural perception | Low | Yes | Yes | Low |
E2LLM is one implementation of runtime structural perception. It captures the rendered page as a structured representation — see SiFR, the format — after the page has actually loaded, in the session the user is already in.