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Java AI Engineering: Enterprise RAG & Autonomous Agents
Rating: 4.5 out of 5(1 rating)
12 students

Java AI Engineering: Enterprise RAG & Autonomous Agents

Build scalable RAG pipelines, vector search, and autonomous tool-calling agents for enterprise Java applications.
Created byModern Professional KI-Akademie
Last updated 1/2026
English

What you'll learn

  • Architect Retrieval-Augmented Generation (RAG) pipelines to ground AI responses in private enterprise data.
  • Implement high-performance semantic search using vector embeddings and vector databases.
  • Enable autonomous function-calling, allowing LLMs to execute Java methods for real-world business actions.
  • Build complex agentic loops using reasoning models that perceive, plan, act, and reflect.

Course content

4 sections • 17 lectures • 1h 12m total length

  • Semantic Foundations: Quantifying Text Meaning in Java AI0:38

    Welcome to the course. To follow along with the demonstrations, please download the exercise files from the Resources tab of this lecture. Let’s get started.

  • The RAG Blueprint: Architecture and Embedding Essentials8:11
  • LangChain4j Implementation: Generating High-Quality Embeddings7:59
  • Hands-on: Implementing Semantic Similarity Calculations2:53
  • Semantic Engineering: Embeddings and RAG Architecture

Requirements

  • Completion of "Java AI Foundations" or a solid grasp of LangChain4j basics.
  • Basic understanding of databases (SQL/NoSQL) and REST API principles.

Description

This course contains the use of artificial intelligence.

In the professional landscape of 2025, simply calling an LLM API is no longer enough. To deliver true business value, AI must be grounded in reality and capable of taking action. This course, "Enterprise RAG & AI Agents: Advanced Java LLM Workflows," is a strategic deep dive into building production-ready AI systems that integrate seamlessly with your private data and business logic.

We begin by architecting the modern Retrieval-Augmented Generation (RAG) pipeline. You will master the complex workflow of data ingestion: from loading diverse document sources (PDF, Word, Text) to parsing and strategic chunking. We explore the mathematical foundations of semantic similarity and vector embeddings, teaching you how to store and retrieve high-quality context from professional vector databases like Milvus, Pinecone, or Qdrant. This ensures your Java applications provide accurate, trustworthy responses while eliminating the risk of AI hallucinations.

Beyond data retrieval, we move into the era of Agentic AI. You will learn to bridge the gap between AI reasoning and actual execution through Function-Calling. By using LangChain4j’s high-level tool primitives and the @Tool annotation, you will empower Large Language Models to call your Java methods autonomously to solve complex, multi-step tasks. We cover advanced agent architectures—including Sequential, Parallel, and Loop workflows—giving you the blueprint to build systems that don't just "talk," but "act" with professional precision. Whether you are a Senior Engineer or a Software Architect, this course provides the technical expertise required to lead AI transformation in an enterprise environment while maintaining strict security and reliability standards.

Who this course is for:

  • Senior Java Engineers building secure, data-grounded AI solutions for corporate environments.
  • Enterprise Architects looking to automate complex workflows through autonomous AI agents.
  • AI Product Managers who need to understand the technical constraints and ROI of RAG systems.

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