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In this video, I walk you through what AI Agents really are, how Agentic RAG (Retrieval-Augmented Generation with reasoning + actions) works, and how to build a fully functional AI Agent using Azure AI Foundry. You’ll learn: 🧠 The basics of AI Agents — perception, reasoning, and acting in an environment 🔄 How ReAct (Reason + Act) works inside an AI Agent 📂 Connecting your own documents to your AI Agent with RAG ⚙️ Deploying GPT-4.1 as your agent’s “brain” in Azure AI Foundry 🏒 A real-world demo: building a hockey stats agent that answers player-specific questions 🐍 How to interact with your AI Agent using Python 🌐 How to call your agent using cURL commands from your local machine 💡 Tips for production setups, security, and observability Whether you’re an AI enthusiast, developer, or architect, this tutorial will give you a solid foundation for building intelligent, document-connected agents—fast. 🎥 Previous video: • Step-by-Step Guide to RAG with LLMs Using ... 🎥 Watch next: • Agentic RAG at Scale with Azure AI Search ... ⏱ Timestamps 00:00 Intro – AI Agents & Agentic RAG 00:43 AI Agent basics explained 02:38 Creating an Agent in Azure AI Foundry 03:15 Deploying GPT-4.1 as the LLM brain 04:48 Real-world hockey stats example 06:46 Connecting documents (RAG) 11:16 Using the agent in Python 15:15 Using the agent with cURL 20:18 Wrapping up & next steps 💬 Have questions about building AI Agents? Drop them in the comments, and I might cover them in the next video. 📌 Subscribe for more AI + cloud development tutorials! #AI #AzureAI #RAG #AgenticRAG #AIagents #Azure #LLM #GPT4 #ArtificialIntelligence #CloudDevelopment #MachineLearning