Why AI agents guess instead of searching the web
An opinion piece published by The Next Web and written by web-data vendor Decodo argues that AI agents make an invisible "fetch decision" before answering — replying from training memory or going out to search and read a live page. It says lookups cost tokens and seconds and often break when sites block automated traffic, require JavaScript or return only a cookie-consent banner, while recall is nearly free; because product teams reward speed and smoothness, memory-based answers quietly become the default, and a model has no sense of how old a remembered fact is — a 2024 hotel rate carries the same undated certainty as the boiling point of water. The author's suggested fixes are to measure how often an agent actually reads a page, by topic; force a lookup for prices, stock, legal and financial questions; tell users when a check failed; and cut handoffs between search and fetch services. The closing section promotes Decodo's Web Data API, which the company says handles search, site crawling and single-page extraction with one API key using an ethically sourced IP pool — a vendor claim rather than an independently verified result.
For buyers and developers, the question is less whether an agent can browse than whether it does so consistently — and whether users can tell when a lookup failed.