Live data grounds agent prices

A product price is one of the easiest ways for an AI agent to sound confident and be wrong. In Apify’s tests, two models without tools matched the live price for just 2 of 15 products. Turning on web search helped, but the models still gave wrong prices in every test run. Some answers even linked to a page for a different product.

The practical point isn’t simply “give the agent live data.” It needs to check that the data is for the right product, seller and country, and make clear when a check fails. Apify’s post describes an approach where code, not the model, puts the fetched price into the answer. That matters if your business shows prices to customers or uses them in decisions. A plausible wrong number can be harder to catch than an obvious error. The tests are limited, and this isn’t evidence that every AI agent has a price problem. The item had 15 points and no comments on Hacker News when it was collected, so treat it as an early practical concern, not a widespread shift.

If you’re adding AI to a workflow, make the source and failure status part of the design, not an afterthought. That’s the kind of thing we build into an AI command centre, where people can keep track of what systems do and where their information comes from. Read Apify’s explanation and tests.