The article argues that AI agents need structured, accurate product descriptions beyond marketing slogans to make reliable recommendations, and questions who should provide and verify such data.
We have always attempted to view this as a recommendation question. But in fact, it is not so. This is first and foremost a question of understanding the product itself. If an AI agent is to assist users in choosing tools, services, application programming interfaces, or software, then they need to understand the true essence of the product - and not just randomly select those marketing slogans that rank high. Today, most product information is scattered across various pages, including login pages, pricing pages, documents, frequently asked questions, case studies, comparison pages, and support articles. For humans, this situation is still acceptable. But for salespeople, it is simply a fog. An agent needs more accurate information: \- What problems does the product solve? \- What groups of people is it actually most suitable for? \- Who should not use it? \- What is the scope of its functions? \- What is its price and what are the limitations? \- What are the integrations, APIs, trials, and support models? \- How is it different from alternative products? \- What are the known drawbacks, adoption obstacles, and conversion costs? Because if salespeople cannot accurately understand the product, then the recommendation content will become meaningless. They will list outdated content, repeat the promotional information of the supplier, ignore the limitations, and confidently recommend tools that do not suit the user's situation. So the real question is not merely "How can we make agents recommend us?" The more difficult question is: What exactly will the true situation of the product be that the agent can understand? Should the company release structured product descriptions for agents? Who should provide this information - the supplier, a third party, or all three? How should agents verify its freshness and accuracy? And how should they handle biased comparison pages or outdated pricing information? Can we eventually see something like "robots.txt" (an instruction file for website crawlers), that is, a standard place where agents can find product descriptions, limitations, pricing, policies, and supporting materials? If agent services become part of software and service discovery, relying solely on marketing copy is not enough. The product needs to be identifiable by machines, but not turn the entire network into another layer of optimized spam.
The article discusses the emerging challenge of making products easily understandable to AI agents, distinguishing it from traditional SEO and highlighting the need for structured data and clear functional boundaries.
Discusses the challenges AI agents face when recommending products from multiple information sources, each with its own biases and limitations, and questions how to design a trust layer for reliable recommendations.
The article raises design and ethical questions about what information AI agents should disclose when recommending products or services, including business partnerships, ranking criteria, and affiliate relationships, drawing parallels with traditional online advertising transparency patterns.
The article explores the ethical and commercial dilemmas surrounding AI agents that make product or service recommendations, questioning how attribution, transparency, and monetization should work without turning agents into covert advertising tools.
The article discusses the need for unified standards and protocols for AI agent recommendations to prevent fragmented, opaque incentive mechanisms and ensure transparency in how agents suggest products or services.