AI has changed the way that people find information. For businesses, this means it's critical to understand how LLMs search for and summarize their web content. We're building an AI Engine Optimization (AEO) system to track how models discover, interpret, and reference Vercel and our sites. This started as a prototype focused only on standard chat models, but we quickly realized that wasn’t enough. To get a complete picture of visibility, we needed to track coding agents. For standard models, tracking is relatively straightforward. We use to send prompts to dozens of popular models (e.g. GPT, Gemini, and Claude) and analyze their responses, search behavior, and cited sources.AI Gateway Coding agents, however, behave very differently.

Many Vercel users interact with AI through their terminal or IDE while actively working on projects. In early sampling, we found that coding agents perform web searches in roughly 20% of prompts. Because these searches happen inline with real development workflows, it’s especially important to evaluate both response quality and source accuracy. Measuring AEO for coding agents requires a different approach than model-only testing. Coding agents aren’t…