AI Agents Explained: ReAct, Memory, MCP & Multi-Agent Patterns

A plain LLM can only do one thing: answer a prompt, once, from memory. It can't fetch live data, take real actions, or recover when something goes wrong. AI agents fix all three — and in this video, we build the entire picture up from scratch, with diagrams at every step. This is a direct continuation of our LLM Foundations and RAG videos — if you haven't seen those yet, start there first. 🔹 What you'll learn: Why agents exist, and exactly what a plain LLM can't do on its own What an agent actually is: Goal + Memory + Tools + Reasoning, running in a loop LLM vs Agent, and Agent vs Workflow — two comparisons people constantly mix up The ReAct pattern (Reason → Act → Observe), traced through a real example The 4 types of agent memory: short-term, long-term, episodic, and semantic A full worked example: an agent handling "compare our top 3 competitors on pricing" The architecture build-up: Bare LLM → + Tools → + RAG → + MCP MCP (Model Context Protocol) — what it actually is, explained properly 6 multi-agent patterns: Supervisor, Hierarchical, Peer-to-Peer, Pipeline, Parallel, and Reflection Critic 📋 Full glossary of every term covered: pinned comment below. #AIAgents #LLM #ReAct #MCP #MultiAgent #AI #MachineLearning