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How AI Agents Use Websites, Where They Fail, and What to Fix

As browser-based AI agents begin navigating real websites, many failures go unnoticed and can silently distort analytics or break critical journeys. This research argues that accessibility engineering provides much of the practical foundation for agent-compatible interfaces, but readiness scores and benchmarks miss what matters - task-level outcomes on your own site. It outlines a failure pipeline and a monitoring approach to measure where agents go wrong.

Research / AI Search / Best Practice

Giacomo Zecchini
Giacomo Zecchini

How Brave Search discovers new pages: A deep dive into the Web Discovery Project

Claude's web search runs on Brave, and 79% of its citations come from Brave's top 10 results. By reading Brave's open-source Web Discovery Project client, we traced exactly how pages enter that index: through a hard door requiring approximately 20 distributed users, or an easy door triggered by a single search on Google or Bing. The findings reveal specific, testable rules for making your site discoverable to Brave and, downstream, to Claude.

AI Search

Ryan Siddle
Josh Blyskal
Ryan Siddle and Josh Blyskal

The agent-readable web: serving the right representation to the right crawler

Most web content is still built for browsers, but agents increasingly need a cleaner representation of the same underlying content. Rather than creating a parallel web of separate Markdown URLs, the better model is HTTP content negotiation: one canonical URL that returns HTML for browsers and Markdown for agents that ask for it. This piece lays out a practical rollout covering .md endpoints, caching, and the fidelity checks that determine whether the representation actually improves retrieval.

AI Search

Ryan Siddle
Will Nye
Ryan Siddle and Will Nye

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