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