Agentic Web Data Extraction at Scale: When Agents Replace Scrapers
· 10 min read
The demo takes 20 minutes to build. You paste a URL, an LLM reads the HTML, and structured data comes out the other end. It feels like the future of web extraction has arrived.
Then you run it at 1,000 pages per hour. Costs spiral, blocks accumulate, and extracted fields start drifting in ways that don't look like errors — they look like normal data until your downstream pipeline has silently ingested three weeks of garbage. The "LLM reads the page" pattern is not wrong; it's just priced for prototype throughput.
Agentic web extraction genuinely solves problems that traditional scrapers cannot. But scaling it past proof-of-concept requires understanding a different set of failure modes than most teams expect.
