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.
Over the years, the product team has been dedicated to optimizing the performance of search engines. They aim to make websites easily crawlable by search engines. They write appropriate page content. They clearly define categories. They help Google understand your product. However, as more and more users start demanding that AI agents search for tools, compare services, or select products, the issue changes slightly: Can this agent accurately understand the actual functions of your product? This is not exactly the same as SEO. Agents may not care as much about meticulously designed positioning strategies, but rather focus on aspects such as: \- Clear functional boundaries \- Easily interpretable pricing methods \- Structured documents \- Clear API functions \- Best applicable and non-applicable use cases \- Objective comparison standards \- Trustworthy user feedback \- Machine-readable metadata Of course, the risk is that "agent discoverability" could become yet another spam game. So, perhaps the real challenge lies in two aspects: The product needs to make itself more understandable to agents, while agents need to avoid being manipulated by content written merely to please machines. To find out what others think about this. Will the discoverability of agents become a real consideration in the product/growth goals, or is it just a new guise of search engine optimization? When agents search for tools, compare services, or select products, the issue changes slightly: Can this agent accurately understand the actual functions of your product? This is not exactly the same as SEO. Agents may not care as much about meticulously designed positioning strategies, but rather focus on aspects such as: \- Clear functional boundaries \- Easily interpretable pricing methods \- Structured documents \- Clear API functions \- Best applicable and non-applicable use cases \- Objective comparison standards \- Trustworthy user feedback \- Machine-readable metadata Of course, the risk is that "agent discoverability" could become yet another spam game. So, perhaps the real challenge lies in two aspects: The product needs to make itself more understandable to agents, while agents need to avoid being manipulated by content written merely to please machines.
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.
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.
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 ethical questions about transparency in AI-generated content, such as novels and websites, and whether consumers should be informed when AI is used in creative or commercial work.
The article criticizes the trend of 'tokenmaxxing' as a vanity metric for AI adoption and presents a coherent AI policy that emphasizes understanding AI-generated code, self-sufficiency without AI tools, and a focus on customers and teammates.