AI Agents Explained in Java – LangChain4j, React & RAG (End to End)

AI Agents are replacing traditional chatbots — and Java developers can’t ignore this anymore. In this video, you’ll build real AI agents in Java using LangChain4j, ReAct, RAG & Spring Boot. Join WhatsApp: https://www.whatsapp.com/channel/0029Va8fH154IBhEu3t21y2o 👉Get CloudWays ➜ https://www.cloudways.com/en/?id=1365224 💥CloudWays COUPON CODE: CLOUDGURU25 ☝️☝️ USE THE EXCLUSIVE COUPON CODE ABOVE TO GET 25% OFF FOR 3 MONTHS💥 👉Get Digital Ocean ➜ digitalocean.pxf.io/ZQERvQ 💥Get $200 FREE Credits for signup. So, hurry up!💥 ╔═╦╗╔╦╗╔═╦═╦╦╦╦╗╔═╗ ║╚╣║║║╚╣╚╣╔╣╔╣║╚╣═╣ ╠╗║╚╝║║╠╗║╚╣║║║║║═╣ ╚═╩══╩═╩═╩═╩╝╚╩═╩═╝ AI Agents are not just prompts or chatbots — they reason, act, retrieve knowledge, and make decisions. This video breaks down AI Agents in Java from scratch to production-ready architecture. 🔥 What you’ll learn: What AI Agents actually are (vs chatbots & LLM wrappers) How ReAct Agents reason and take actions How RAG (Retrieval Augmented Generation) works internally Using LangChain4j to build agent workflows in Java End-to-end Spring Boot AI Agent architecture Real-world use cases for enterprise & startups 🧠 Who is this for? Java developers entering AI / GenAI Backend & Spring Boot engineers Software architects & tech leads Anyone building production AI systems 🛠️ Tech Stack Used: Java LangChain4j ReAct Agents RAG (Vector Search + LLMs) Spring Boot 💬 Comment below if you want: Full source code Architecture diagrams A follow-up video on multi-agent systems #AIAgents #JavaDeveloper #LangChain4j #GenerativeAI #SpringBoot #AIEngineering #LLM