So you’re building an AI agent. At first, everything feels simple. The user asks a question, the model thinks, and the agent returns an answer. Then you hit the obvious problem: The agent needs fresh information. Maybe it needs today’s search results. Maybe it needs current competitors. Maybe it needs recent product pages. Maybe it needs local business results from a specific city. Maybe it needs sources before writing a research summary. A language model can reason well, but it does not always know what is happening right now. If the task depends on current search results, you need a search layer. For many agent workflows, that search layer starts with Google results in JSON. In this post, we’ll walk through a simple way to think about it: User task → search query → SERP API → JSON results → AI agent response Enter fullscreen mode Exit fullscreen mode What we are building Let’s say we want to build a basic research agent.…