Alex Soto: AI Agents Graph: Your following tool in your Java AI journey - Codemotion 25
Large Language Models (LLMs) have recently become increasingly popular. These models can be incorporated into your enterprise code to enhance the customer experience or automate business operations. In any use case involving AI, you’ll rapidly encounter the concept of an AI agent, the code responsible for determining the following steps. LangChain4J is an example of implementing AI agents, a sequence of Java calls and logic interacting with a model. For simple use cases, it might work, but step by step, as the code becomes more complex, you’ll end up with unmaintainable spaghetti code with business logic and AI logic mixed together. Tools like LangGraph offer a structured and efficient framework for defining, coordinating, and running multiple LLM agents using cyclical graphs. Come to this session to learn how graph networks can help you implement advanced use cases and explore their integration with LangChain4j to create robust, efficient, and versatile AI applications. Puedes seguir a Codemotion en sus redes sociales: @Codemotionworld // https://www.linkedin.com/company/codemotion/ // https://twitter.com/CodemoMadrid // https://www.instagram.com/codemotion_esp/ ------- Síguenos en nuestras redes sociales Web: https://sirviendocodigo.com/ LinkedIn: https://www.linkedin.com/company/sirviendo-codigo/ X: https://twitter.com/sirviendocodigo Instagram: https://www.instagram.com/sirviendo.codigo/ TikTok: https://www.tiktok.com/@sirviendo.codigo #LLM#AI #Langchain4J #ArtificialIntelligence #sirviendocodigo
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