Relying on AI agents has become an essential part of the daily search journey for developers and software engineers. Recent polls, such as the 2026 Developer Survey, indicate that about 70% of participants use AI agents to help find appropriate answers and technical solutions. This demographic shift in search behavior presents a new challenge for tech companies and software development platforms: how to ensure their tools and platforms appear in the top answers provided by Large Language Models (LLMs)?
Web site development and content management platform Webflow studied and analyzed the behavior of AI engines by tracking hundreds of queries directed at several major models, such as Anthropic's Claude, OpenAI's ChatGPT, Google's Gemini, and Perplexity. The study aimed to determine how often the platform's name appeared when specific questions were asked regarding software integration with protocols such as Model Context Protocol (MCP), and the available options for web building and content management.
What do the practical data reveal?
Data collected by Webflow over a specific period, tracking 100 query responses, showed wide variation among different AI engines. For example, Perplexity led in terms of name repetition, appearing about 22 times out of 25 responses, followed by ChatGPT, while other models came in with lower rates. However, the technical team noted that simply mentioning the platform's name does not necessarily mean the description or classification is accurate; some models provided precise descriptions showing that the MCP server comes with OAuth authentication support and was developed by the platform itself, while other models considered it a third-party integration or failed to provide accurate details.
The analysis also showed that the sources relied upon by the models differ radically. While some models depended on official corporate documentation and their proprietary technical documents, the majority relied on external guides, technical blogs, and third-party repositories, making the platform's appearance sometimes dependent on external references rather than direct documentation.
Why does this development matter to technical teams?
This strategic shift is of utmost importance to tech companies and software products for several practical reasons:
- Absence of traditional visits: When an AI agent provides a direct answer to a developer, the user may not visit the company's website or see traditional analytics results, making the measurement of promotional impact limited to direct monitoring of responses.
- Accuracy of description and classification: Getting a mere passing mention of the name is not enough; more importantly, technical capabilities must be described accurately (such as the nature of supported MCP servers and available integrations) to avoid misleading developers.
- Controlling sources: Relying on official documentation and direct software documents increases the chance of models citing correct information instead of external articles that might be outdated or inaccurate.
This experiment confirms that improving product visibility in the AI era is no longer limited to traditional search engine optimization (SEO) for page visibility, but extends to what is known as AI Engine Optimization (AEO), which requires continuous tracking of responses, understanding how models interpret technical capabilities, and working to provide structured and reliable software documentation that agents can easily understand and use.