Agentic Engineering & ToolingConference50min
A Year of Agentic AI Evolution: Lessons Learned Building Production-grade Agentic Systems
LangChain4j’s agentic framework has evolved from fixed patterns to flexible orchestration, letting developers compose strategies for different use cases. The session covers new built-in patterns, their trade-offs, and production features like tracing, state persistence, failure recovery, and human-in-the-loop pauses.
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Mario FuscoIBM
Kevin DuboisIBM
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Wednesday, October 7, 14:00-14:50
TBA 7
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Last year at Devoxx, we introduced the first version of LangChain4j's agentic framework and explored the core patterns behind agentic AI systems.
Since then, real-world usage, community feedback, and a lot of experimentation have taught us that production-ready agentic systems need more than a fixed set of predefined patterns. Different use cases need different trade-offs, and a predefined list can only take you so far.
In this session, we'll show how LangChain4j's agentic support has evolved. We have moved toward a more flexible orchestration model that lets developers compose their own strategies, while also allowing us to grow the library of built-in patterns. We will demonstrate in practice how these new patterns work discussing their respective pros and cons and fields of applicability.
We'll also cover the operational capabilities added to the framework to make these workloads production-ready. You'll see how to observe and trace agent execution, persist state, recover long-running workflows after failures, and handle human-in-the-loop pauses without losing control of the process.
Join us as we demonstrate these new features along with their trade-offs, and learn what it takes to build robust, production-ready agentic systems.
Since then, real-world usage, community feedback, and a lot of experimentation have taught us that production-ready agentic systems need more than a fixed set of predefined patterns. Different use cases need different trade-offs, and a predefined list can only take you so far.
In this session, we'll show how LangChain4j's agentic support has evolved. We have moved toward a more flexible orchestration model that lets developers compose their own strategies, while also allowing us to grow the library of built-in patterns. We will demonstrate in practice how these new patterns work discussing their respective pros and cons and fields of applicability.
We'll also cover the operational capabilities added to the framework to make these workloads production-ready. You'll see how to observe and trace agent execution, persist state, recover long-running workflows after failures, and handle human-in-the-loop pauses without losing control of the process.
Join us as we demonstrate these new features along with their trade-offs, and learn what it takes to build robust, production-ready agentic systems.
Mario Fusco
Mario is a senior principal software engineer at IBM working as Drools project lead. Among his interests there are also high performance systems and generative AI, being an active contributor of widely adopted projects like Quarkus and LangChain4j. He is also a Java Champion, the JUG Milano coordinator, a frequent speaker and the co-author of "Modern Java in Action" published by Manning.
Kevin Dubois
Kevin Dubois is a software architect and platform engineer with a career spanning over 20 years. He is often featured as a keynote speaker at conferences around the world where he shares his experience and knowledge about cloud native & AI software development, developer experience, open source and Java. Kevin is also an author and Java Champion. He currently works as a Senior Principal Developer Advocate at IBM, and is Technical Lead for the CNCF Developer Experience Technical Advisory Group as well as an ambassador for the Agentic AI Foundation.