AI Agents Explained in Java | LangChain4j, ReAct Agents, RAG & Spring Boot (End-to-End)

In this video, we deep dive into how modern AI agents actually work - beyond simple chatbots. Using Java + Spring Boot + LangChain4j, we break down: - What an AI Agent really is? - How LLMs decide when to call tools - Why every agent call involves at least one LLM API call - How Chat Memory maintains conversational context - How RAG (Retrieval-Augmented Generation) works internally - Embeddings, Vector Stores, and Content Retrieval explained step-by-step 📌 What You’ll Learn ✔ AI Agents vs Chatbots ✔ ReAct (Reason + Act) pattern explained clearly ✔ Tool execution lifecycle ✔ Why tools never run without the LLM ✔ How RAG grounds AI responses ✔ Performance vs complexity trade-offs ✔ How to architect AI agents cleanly in Spring Boot 🔗 Source Code (GitHub) 👉 GitHub Repo: https://github.com/raghuveer-rohine/Langchain-Demo 🔗 Connect with Me 👉 LinkedIn: https://www.linkedin.com/in/raghuveer-rohine-349505176/ 🚀 My Udemy Course If you’re interested in deploying Spring Boot microservices securely on AWS ECS, check out my Udemy course: 👉 Deploying Spring Boot Microservices on AWS ECS (Secure & Production-Ready) https://www.udemy.com/course/deploying-java-spring-boot-microservices-on-aws-ecs-securely/?couponCode=MT250908G2 ⏱️ Who is this video for? - Java & Spring Boot developers - Backend engineers exploring AI - Engineers learning LangChain4j - Anyone building AI-powered backend systems 👍 If you found this helpful Don’t forget to like, share, and subscribe for more deep-dive backend + AI content. 🔖 Hashtags #AIAgents #LangChain4j #Java #SpringBoot #RAG #ReActAgent #LLM #AIEngineering #BackendDevelopment #Microservices #AWS #ECS #UdemyCourse #VectorDatabase #Embeddings