Emmanuel Hugonnet
Developer at IBM
Développeur pour IBM
Barraux, France
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Into the Java World for the last 25 years, I joined Red Hat in 2013 to contribute on the development of the JakartaEE server WildFly.
I also contribute to a large number of OSS projects and I'm a member of the Apache NetBeans Dream Team.
I've been one of the founder of the SnowCamp conference and I'm a JUG leader of AlpesJUG.
Recently I've started to look into Generative AI and LangChain4J and I started the wildfly-ai-feature-pack to facilitate developing Generative AI JakartaEE applications with WildFly, contributing to langchain4j-cdi and LangChain4J along the way.
Dans le monde Java depuis plus de 25 ans, j'ai rejoint Red Hat en 2013 pour participer au développement du serveur d'applications JakartaEE WildFly.
Je contribue aussi régulièrement à un grand nombre de projets libres, et je suis un membre de l'Apache NetBeans Dream Team.
J'ai été l'un des fondateurs de la conférence SnowCampet je suis l'un des JUG leaders de AlpesJUG.
Récemment je me suis intéressé à l'IA Générative et à LangChain4J et j'ai démarré le wildfly-ai-feature-pack pour faciliter le développement d'applications JakartaEE intégrant de l'IA Générative avec WildFly, contribuant à langchain4j-cdi et à LangChain4J chemin faisant.
Area of Expertise
Topics
SmallRye-LLM: Integrate Generative AI in your JakartaEE Applications en
This talk will present how smallrye-llm integration in WildFly makes LangChain4J easy to use in your enterprise JakartaEE applications.
Langchain4j-CDI: Inject Generative AI in your JakartaEE/MicroProfile Applications en fr
LangChain4j-CDI: Generative AI in JakartaEE
With the rise of generative AI, Java is no longer lagging behind. LangChain4j is a game changer: Java is cool again thanks to AI!
Emmanuel, industry veteran, present LangChain4j-CDI: the ultimate CDI extension to infuse AI into your JakartaEE/MicroProfile applications.
AI Injection as Simple as an EntityManager
Imagine injecting an AI agent as naturally as an @Inject EntityManager. With @RegisterAIService, you can transform your applications into intelligent systems without boilerplate code, while preserving enterprise best practices.
AI Agent Architecture
Learn how to build an AI agent inside a JakartaEE container using:
- MCP Protocol: connect to tools that are themselves JakartaEE applications
- A2A Protocol: orchestrate agents through the standard protocol
Enterprise-Grade Robustness
Ensure resilience and traceability with:
- Multi-provider configuration via MicroProfile Config
- Full observability (metrics, traces, logs)
- Resilience with MicroProfile Fault Tolerance
Spectacular Live Coding
Quarkus, WildFly, Helidon: we will code and deploy an AI agent in under 5 minutes flat!
Injectez l'IA Générative dans vos Architectures d'Entreprise : LangChain4j-CDI ! en fr
LangChain4j-CDI : L'IA Générative dans JakartaEE
Après l'irruption de l'IA générative, Java n'est plus en retard. LangChain4j change la donne : Java est redevenu cool avec l'IA!
Emmanuel, vétéran de l'industrie, vous présentent LangChain4j-CDI : l'extension CDI ultime pour infuser de l'IA dans vos applications JakartaEE/MicroProfile.
Injection d'IA Aussi Simple qu'un EntityManager
Imaginez injecter un agent IA aussi naturellement qu'un @Inject EntityManager. Avec @RegisterAIService, transformez vos applications en systèmes intelligents sans code boilerplate, tout en conservant les bonnes pratiques d'entreprise.
Architecture d'Agents IA
Découvrez comment coder un agent IA dans un conteneur JakartaEE avec :
Protocole MCP : connexion à des outils eux-mêmes applications JakartaEE
Protocole A2A : orchestration d'agents via le protocol standard
Robustesse Entreprise
Assurez résilience et traçabilité avec :
Configuration multi-providers via MicroProfile Config
Observabilité complète (métriques, traces, logs)
Résilience avec MicroProfile Fault Tolerance
Live Coding Spectaculaire
Quarkus, WildFly, Helidon : nous coderons et déploierons un agent IA en moins de 5 minutes chrono !
Wiring Up the AI Mesh: Building A2A-Compliant Java Agents en
Building Production-Ready A2A Agents with LangChain4j, Quarkus, Spring Boot & Jakarta EE
Enterprise AI is shifting from isolated chatbots to multi-agent ecosystems where specialized agents negotiate and delegate tasks. The Agent-to-Agent (A2A) Protocol is the emerging standard for this interoperability, defining the wire format for seamless cross-language communication.
This intensive, hands-on workshop equips Java developers to build, connect, and scale production-grade A2A ecosystems using LangChain4j and the official A2A Java SDK.
🛠️ What You Will Learn & Build
The journey begins with the core architecture, where you will decode the A2A specifications and implement an AgentCard for schema discovery alongside an AgentCardExecutor for standardized message delivery.
To prove runtime independence, you will build several agent on vairous runtimes( JakartaEE, SpringBoot, Python).
As the network grows, you will dive into orchestration by building a dynamic router agent capable of decomposing complex, multi-turn tasks and dispatching them across your diverse mesh.
Finally, you will prepare for Enterprise Day-2 Operations by mastering production challenges like distributed state management, load balancing for stateful LLM conversations, and tracing complex interactions via OpenTelemetry.
Microsoft JDConf 2025 Sessionize Event
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