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/L... 🔗 Connect with Me 👉 LinkedIn:   / 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/deployin... ⏱️ 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