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

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