Choose your own adventure in agentic design patterns

Time to dive a little deeper, beyond the usual AI agent intros. When building your multi-agent systems, you’ll often be combining basic bricks like sequential, parallel, or loop flows. With those components, you create more complex patterns, like reflection loops, and reviewer/critique agents.

However the path to "AI-nlightment" might require you to make choices with the routing pattern, or to shepherd a swarm of agents to collaborate together. We'll talk about different approach to planning (from programmatic to goal oriented).We'll uncover the concept of progressive disclosure used in skills. We'll see that AI agents can improve baselines with the LLM-as-Judge pattern. And we'll finish with discussing agent loop approaches.

As a committer on both LangChain4j and ADK for Java (Agent Development Kit), it’ll be my pleasure to guide you through this agentic adventure, and help you make the right choices and use the right abstractions on your journey.