From LLM orchestration to autonomous agents: Agentic AI patterns with LangChain4j

LLMs have rapidly evolved from simple chatbots into intelligent components that can reason, plan, and take actions. Moving beyond basic prompt engineering means adopting new design patterns and frameworks that let you stitch together these models into programmatic workflows—think dynamic decision-making, memory retention, tool use, RAG, multi‐step goal execution, and even AI‐to‐AI collaboration.

In this deep dive, you’ll get hands-on with LangChain4j, a Java-native framework built on Quarkus. Through real-world examples and live coding, you’ll learn how to architect and test autonomous agents—complete with modular design, integrated tools, and practical patterns—to tackle everything from task automation to decision‐support systems.

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