Agentic AI Fundamentals: Agents, Tools, Memory, RAG, MCP & Harness Explained

What exactly is an AI Agent? And how is Agentic AI different from a traditional LLM? 🤖 In this video, we build a strong foundation in Agentic AI by breaking down the key terminology, components, architectures, and concepts you need to understand before building AI agents. What you'll learn: 🧠 Model — The brain behind an agent, typically an LLM 📝 Prompt & System Prompt — Instructions that define the agent's behavior 🛠️ Tools — How agents interact with external functions and systems 🔄 Agentic Loop — Think → Act → Observe → Repeat 💾 Memory & Context — Short-term vs. long-term memory 🎯 Context Engineering — Managing the information available to an agent 🔗 Nodes & Edges — Building agent workflows as graphs 👨‍💼 Supervisor & Worker Agents — Coordinating multiple specialized agents 👤 Human-in-the-Loop (HITL) — Adding human approval to agent workflows 📚 RAG & Agentic RAG — Giving agents access to external knowledge 🔌 MCP (Model Context Protocol) — Connecting models with tools and external systems 🏗️ Harness — The infrastructure around the model that enables tools, memory, workflows, and other capabilities The goal of this video is to give you a clear mental model of how modern AI agents are built and how the different pieces fit together. Whether you're a Data Engineer, AI Engineer, Software Engineer, ML Engineer, or developer exploring Agentic AI, this video will help you understand the terminology before diving into implementation. 👍 Like | 💬 Comment | 🔔 Subscribe for more AI Engineering, Data Engineering & System Design content. #AIAgents #AgenticAI #AIEngineering