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Devoxx: From LLM orchestration to autonomous agents: Agentic AI patterns with LangChain4j by Clement Escoffi

From LLM Orchestration to Autonomous Agents with LangChain4j

Over the past few months, LLMs have evolved from simple chatbots into powerful building blocks for programmatic workflows and fully autonomous agents that can plan, reason, and take action. Moving beyond basic prompt engineering means embracing new design patterns and frameworks to turn large language models into dynamic, decision-making components.

In this deep dive, Clement Escoffi shows you how to use LangChain4j—a Java-native framework—alongside Quarkus to build modular agents that remember context, use external tools, perform retrieval-augmented generation (RAG), coordinate multiple agents, and execute multi-step goals. Packed with real-world examples and live coding, you’ll walk away ready to craft maintainable, robust autonomous AI systems in Java.

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