Agentic Browsing
An AI navigating a website on somebody’s behalf: finding the price, checking availability, sometimes buying. Sites that rely on hover menus and unlabelled buttons are hard for an agent to use.
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An AI navigating a website on somebody’s behalf: finding the price, checking availability, sometimes buying. Sites that rely on hover menus and unlabelled buttons are hard for an agent to use.
How much text a model can hold in mind at once, counted in tokens. Anything beyond it is forgotten, which is why very long conversations start to lose earlier details.
The rough limit on how much of your site a crawler will fetch in a visit. Slow pages and endless near-duplicate URLs spend it on nothing, so the pages that matter get visited less often.
An extension to robots.txt that states not just who may crawl, but what they may do with what they find: appear in search, be quoted in answers, be used as training data. It lets you permit citation while refusing training.
Turning spoken words into text. It underpins voice search, dictation and call transcription, and it now handles accents and background noise far better than it used to.
When a model gets worse over time because the world moved on and it did not. A pricing or demand model trained two years ago quietly stops matching how customers behave now.
When a system produces unfair results because its training data reflected an unfair world. It matters commercially as well as ethically: a biased model makes confidently wrong decisions about real people.