I benchmarked how badly AI agents read raw HTML. The gap was bigger than I expected.

Most agents today hit a webpage and get handed 300KB of markup built for human eyes — nav, footers, tracking scripts, styling noise — and then have to guess the three facts they actually need. Price, availability, whether an action charges money. They burn tokens parsing presentation and still get it wrong. I wanted to know how bad it actually is, so I measured it instead of guessing. The test: 20 fact-extraction questions about a single product page, run through real LLM calls. Same page, two formats. \- Raw HTML: 91% accuracy, 684 tokens \- A structured agent-readable format: 100% accuracy, roughly half the tokens The accuracy gap surprised me less than the token cost. Agents are paying double to read the worse version of the page. This led me down a rabbit hole of building a contract layer for it — a way for a site to expose a typed, machine-native view alongside its normal HTML: the entities on the page, plus typed action contracts (does this action cost money, is it reversible, what are the side effects, does it need confirmation). The HTML keeps rendering unchanged for browsers; agents get a clean endpoint. Genuinely curious how others here are handling this. Are you pre-processing HTML before it hits the model, using readability extractors, something else? What's actually working for you in production?