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

The late few months have seen the rapid evolution of LLMs from passive completion engines, only good for generic chatbots, to components of a more complex, programmatically defined, workflow and finally into active and autonomous elements capable of reasoning, planning, and taking actions.

But moving from basic prompt engineering to truly autonomous systems requires a new class of design patterns and possibly a framework allowing to implement those patterns and put them at work in a convenient and effortless way.

In this deep dive, we will explore the architecture and implementation of agentic AI using LangChain4j, a Java-native framework for building LLM-powered applications. You’ll learn how to move beyond the plain usage of a standalone LLM to design intelligent, modular agents capable of dynamic decision-making, memory retention, tool usage, RAG, MCP and A2A integration and multi-step goal execution.

After having covered the core concepts of agentic AI, we will guide you in incrementally building and testing an agentic system from scratch using LangChain4j and Quarkus, backed by real-world examples and live coding. Whether you're exploring agentic AI for task automation, intelligent assistants, or decision-support systems, this session will give you the practical tools and architectural understanding to build robust and maintainable autonomous agents in Java.