How the Proxy Ecosystem Prevents the Spamming of Recommendation Information?

If ai agents become an important channel for product recommendations, the problem of spam-like recommendation information will no longer be a minor issue. This has become one of the most common failure modes. We have seen this film on the internet. Low-quality search engine optimization pages. Fake alliance lists. False reviews. Deceptive advertising networks posing as suggestions. Content written for achieving conversions rather than genuine content. Now apply the same incentive mechanism to the agents. This risk is even more serious because users may be more likely to believe the answers given by the agents rather than random web content. A junk article still looks like a web page. And the junk recommendations that appear in agent recommendations may make people mistakenly believe they are judgment results. This is the danger. So the problem is not how agents recommend products, but how the ecosystem prevents the recommendation layer from becoming another polluted market. The questions I have been repeatedly thinking about are: Should agents have a recommendation quality rating? Should they explain why they recommend this product and what evidence is based on? Should there be restrictions on the number of times the same developer, supplier or provider can appear? Should agents be required to present multiple options rather than just giving a "best" answer? Should false, low-quality or profit-driven recommendation behaviors be punished? The internet has shown us that junk information follows people's attention. If certain people become the new focus of attention, then the same people will also appear in these areas of concentrated attention. The difference is that this time, these junk information may not be as conspicuous as before, but may seem like helpful information.