That’s not adjacent to what AI agents need. That is what AI agents need. Long before LLMs existed, assistive technologies proved the core thesis: Machines can navigate interfaces, semantics can outlive presentation and structure can substitute for vision. Screenshot-based agents spend tokens rediscovering facts the browser already knows. The accessibility tree already contains role, name and state in structured form. In my own agent work, switching from screenshot-based to DOM-native execution cut per-action latency from 2–5 seconds to under 500ms and token cost by an order of magnitude.
Accessibility proved the thesis. Now we need the next layer
The most clarifying realization I had was this: Accessibility had already solved a large portion of the problem agents face. Accessibility gives machines a way to discover interfaces. It exposes available controls, their names, their states and their relationships. But discovery is not execution. The accessibility tree can identify a button named “Checkout” and indicate whether it is disabled. What it cannot provide is a contract for the action itself. For example, what inputs it accepts, what preconditions are required and what state changes it produces.
One emerging response to this gap is WebMCP, which introduces a browser-native way to expose typed capabilities that agents can invoke directly. When a form field has no explicit agent annotation, Chrome’s declarative API derives the parameter description from the associated
