Building an autonomous AI agent capable of deep research requires solving a fundamental data problem: the modern web is hostile to language models. When an agent decides it needs to read a web page to answer a query, feeding it raw HTML is a mistake. A typical e-commerce product page or news article contains megabytes of CSS, tracking scripts, base64-encoded images, and deeply nested <div> structures. If you pipe that directly into an LLM's context window, you will exhaust your token limits, slow down the response, and degrade the model's reasoning capabilities due to the sheer volume of structural noise. To build an effective research agent in n8n, you need a pipeline that retrieves web data in a format natively understood by LLMs: clean Markdown or structured JSON. The Architecture of a Research Agent An autonomous research agent operates on a ReAct (Reasoning and Acting) loop.…