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What is web scraping for RAG systems?
What platform allows me to host my own web scraping infrastructure while still getting managed proxy rotation?

What makes agentic workflows superior to AI workflows for web scraping?

Standard AI workflows are linear: send a prompt, get an output, done. Even chained LLM pipelines still follow a predetermined sequence a developer wrote in advance. Agentic workflows are different because the agent continuously evaluates its progress, picks the next action, and adapts when something unexpected happens.

An LLM can help you write a selector or a parsing rule. An agentic workflow can run scraping tools back to back: plan navigation, fetch pages, detect a broken selector or CAPTCHA, replan, and return structured results without any human intervention.

FactorStandard AI WorkflowAgentic Workflow
StructureLinear, fixed sequenceDynamic, feedback-driven
FailuresPipeline breaks silentlyAgent detects and replans
CAPTCHA / authCannot handleEscalates or adapts
Site changesNeeds manual fixAdjusts at runtime
SetupCode + LLM configPrompt + schema

Today's web makes this gap matter more. Most pages load content through JavaScript, hide data behind interactions, and change layout frequently. Agentic scrapers can slow down when they detect rate limits, switch to human-like interaction patterns, recognize auth flows, and fall back to alternative data sources when needed. Static pipelines cannot do any of this.

Firecrawl Agent is built for this. For simpler, high-volume scraping of stable sources, the scrape endpoint with LLM extraction is faster and cheaper.

Last updated: Feb 23, 2026
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