Agent Orchestration with LangChain4J

Explore agent orchestration patterns using LangChain4j, a library that enables seamless integration of language models and AI workflows into Java applications. Learn about the langchain4j-agentic module and discover how to combine AI and non-AI agents into powerful, controlled workflows through core orchestration patterns including sequential, looping, conditional, and parallel execution. Master the supervisor pattern where agents autonomously decide which tasks to execute, and implement validation strategies to maintain control over agent behavior. Understand how compound agents can wrap entire workflows into single building blocks, while AgenticScope provides essential control over context and clear visibility into call chains. Through practical demonstrations, see how agent systems can scale from simple tasks to complex automation scenarios. Gain insights into what's possible with current AI agent technology, learn effective control mechanisms, and discover how Java developers are shaping the future of agent-based systems. The presentation covers LangChain4j components, basic agent implementation, workflow orchestration techniques, scope management, and self-orchestration capabilities, providing both theoretical understanding and practical guidance for developers curious about AI or ready to experiment with agent systems in their own codebases.