From LLM orchestration to autonomous agents with LangChain4j

In just a few months, large language models have leapfrogged from simple chatbots to full-blown, programmatic workflows—and now they’re becoming autonomous agents that can plan, reason, and act on their own. But to really tap into that power you need more than clever prompts: you need solid design patterns and a framework that makes it easy to build, test, and maintain agents.

Enter LangChain4j: a Java-native toolkit that takes you from “Hello, world” prompts to intelligent agents with memory, tool use, retrieval-augmented generation (RAG), multi-agent coordination, and multi-step goal execution. Backed by Quarkus demos and live coding, this deep dive gives you all the building blocks—dynamic decision-making, modular components, RAG, MCP, A2A integration—to craft robust, autonomous systems for task automation, intelligent assistants, or any decision-support scenario.

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