Agentic RAG Tutorial: RAG That Fixes Its Own Answers

_*Disclaimer:* This video features an AI-generated avatar of one of our own Oxylabs experts, created with their full knowledge and consent. The voice and likeness are their own, recreated with AI. The video itself was produced using AI to make our production process more efficient while keeping the people and expertise behind them genuine._ Your vector database says a product costs $49. The competitor's listing says something else, and neither number is current anymore, which is exactly the kind of gap agentic RAG is built to catch. In this hands-on agentic RAG tutorial, we create a self-correcting RAG agent that knows when to trust its own docs and when to look elsewhere. Naive RAG trusts whatever it retrieves, even when the answer is outdated or missing. This tutorial fixes that with a grader step: the agent checks whether its own retrieval actually answers the question before writing anything, the core idea behind any self-correcting RAG agent. We put this agentic RAG workflow together from scratch with LangGraph. The LangGraph RAG agent starts with a fast, internal knowledge base, and only escalates to RAG with web search when the grade comes back insufficient. That's the real answer to the agentic RAG vs. RAG question, since the agent decides which source to trust rather than defaulting to the same one every time. You will see the whole thing come together, starting with the state graph and the retrieval grader, then live web scraping through Oxylabs to pull fresh data the moment the internal docs fall short. Along the way, we also fix a common mistake: feeding raw, unparsed scraping results straight to the LLM. From there, we cover what it takes to run this pattern in production, from freshness thresholds to logging every decision it makes. 🔧 *TRY OXYLABS ROTATING PROXIES* Web Scraper API: https://oxy.yt/LWG1 📚 *RESOURCES MENTIONED* LangGraph documentation: https://oxy.yt/xWHY Oxylabs Web Scraper API docs: https://oxy.yt/PWJl 📖 *RELATED BLOG POST* Agentic RAG Explained: https://oxy.yt/6WK9 🎥 *RELATED VIDEOS* What Is RAG? https://oxy.yt/KWL6 💬 *CONNECT WITH OXYLABS* Website: https://oxylabs.io LinkedIn: https://www.linkedin.com/company/oxylabs-io X: https://x.com/Oxylabs_io Support: [email protected] ⏳ *TIMESTAMPS* 00:00 Proof of life 01:24 What is RAG (and where it breaks) 02:11 What is agentic RAG 03:09 Setup and prerequisites 04:28 Building the internal knowledge base 06:21 Building the LangGraph workflow 07:47 The retrieval grader 09:18 Adding live web data 10:41 Full demo: internal vs. live web 12:32 Production considerations 13:52 Wrap-up #AgenticRAG #RAG #LangGraph #Oxylabs #2026 © 2026 Oxylabs. All rights reserved.