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Explore the evolution from basic LLM usage to building sophisticated autonomous AI agents in this comprehensive conference talk from Devoxx. Learn how to transition from simple prompt engineering to creating truly autonomous systems that can reason, plan, and take independent actions using LangChain4j, a Java-native framework for LLM-powered applications. Discover essential design patterns for agentic AI and understand the architectural principles behind intelligent, modular agents capable of dynamic decision-making, memory retention, tool usage, RAG (Retrieval-Augmented Generation), MCP (Model Context Protocol), and A2A (Agent-to-Agent) integration. Follow along as the speaker guides you through incrementally building and testing an agentic system from scratch using LangChain4j and Quarkus, with real-world examples and live coding demonstrations. Gain practical tools and architectural understanding necessary for developing robust and maintainable autonomous agents in Java, whether for task automation, intelligent assistants, or decision-support systems.
