Build Web-Aware AI Agents in n8n Using Clean Markdown Extraction
The Token Economics of HTML vs. Markdown
Autonomous AI agents require access to real-time web data to make informed decisions. However, the standard approach of feeding raw HTML directly into a Large Language Model (LLM) is a critical architectural f...
alterlab.hashnode.dev9 min read
The n8n setup is definitely doable, but the article kinda glosses over how extraction APIs actually perform in the wild. Sure, 100% success sounds nice in theory, but you're realistically looking at 85-90% on heavily-protected sites, so don't go in expecting magic. To make this actually bulletproof, I'd throw in a cost-benefit decision matrix since extraction APIs run about $30K/month at scale, though they often pay for themselves in LLM savings. Also, semantic validation that goes beyond just checking if your JSON is structurally valid - because LLMs will happily generate correct JSON that makes zero sense contextually, and real safety rails like URL deduplication and iteration caps so you don't accidentally spawn an infinite loop at 3am. The RAG chunking part also needs a reality check, semantic splitting only actually works if your extraction APIs are spitting out well-organized Markdown with proper header hierarchy, and a lot of them aren't. And drop in some war stories from production because those hit different - infinite pagination traps, APIs that randomly drift after weeks of working fine, or suddenly exploding costs because URL volumes spiked