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In this tutorial, we build a real AI research agent from scratch — not a chatbot, not a demo. Using LangGraph, Tavily, and OpenAI, we design a goal-driven agent that searches the web, extracts forward-looking AI predictions, clusters similar signals, scores trends based on trust and consensus, and presents everything in a clean Streamlit dashboard running locally. You’ll learn how agentic workflows actually work in practice: planning actions, using tools, looping intelligently, and synthesizing insights into ranked trends. By the end, you’ll have a reusable AI trend intelligence agent you can adapt for research, content, or decision-making. Here is the complete GitHub repo you can follow: https://github.com/pavanbelagatti/Agentic-AI-LangGraph-Tutorial