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LangChain4j: Building Intelligent Agents
Explore agent fundamentals, LLM/RAG limits, and hands‑on Java LangChain4j examples building agents that trigger actions, plus Model Context Protocol integration.
In this session, we will first start by defining what agents are, or at least what makes a system “agentic”. We will explain what the limitations of LLMs and RAG are. Then, through concrete examples, we will implement different agents in Java, using the LangChain4j framework, to illustrate some typical agent patterns and to understand how to go beyond a simple call to an LLM to get answers that will meet the needs of your users, or even to trigger actions with the surrounding system. We will also discuss the importance of Model Context Protocol (MCP) to extend LLMs with new tools.