AI Agents are gradually being integrated into developers' workflows, but the degree of adaptation varies significantly across different products. Addy Osmani, Director of AI Engineering at Google Cloud, introduced the concept of AEO (Agent Engine Optimization), which aims to optimize products to better meet the needs of AI Agents, analogous to SEO (Search Engine Optimization). AI Agents differ significantly from humans when reading documents: they make rapid judgments through a single HTTP request and are particularly sensitive to the content of the first 500 Tokens, needing to answer core questions within this range. Meanwhile, AI Agents have a Token limit, and exceeding it may result in information loss or content fabrication. Additionally, they do not focus on UI design; redundancy in HTML format consumes Tokens, leading many product documents to be silently rejected by AI Agents. These visits are often classified as low-quality traffic in traditional analytics. To address these issues, Osmani proposed a six-layer AEO optimization framework: first, check the robots.txt file to ensure AI Agent crawlers are not mistakenly blocked; second, publish an llms.txt file as an Agent-version sitemap to guide them to precisely locate the required documents; third, write a skill.md file to declare product capabilities, helping Agents quickly determine compatibility; fourth, provide open access to Markdown versions of documents to reduce Token consumption and avoid content inaccessibility caused by JS rendering; fifth, expose Token counts as metadata to allow Agents to assess document value in advance; finally, add a Copy for AI button to provide clean contextual content. These optimizations do not need to be implemented all at once, and most can be completed in a short time. Notably, AEO optimization does not come at the expense of human experience but rather highly overlaps with high-quality human document design, except that AI Agents have lower tolerance for errors. Currently, a significant amount of AI Agent traffic is misclassified as spam. These Agents select products solely based on usability. This is advantageous for truly excellent products, as it will prompt product teams to redefine the criteria for a 'good product,' requiring greater investment in API design, document structure, and Agent accessibility in the future.