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World Congress 2025 • Aug 20, 2025 • Session details

AI Agents Graph: Your following tool in your Java AI journey

Alex Soto

Bloated prompts kill enterprise AI latency and ruin reliability. Discover how to build resilient, stateful multi-agent workflows in Java using LangGraph4j's graph-based execution.

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#1 about 2 min

Introduction to AI agents and enterprise complexity

Simple agents lack the capabilities needed to handle complex enterprise requirements.

#2 about 2 min

Choosing Java over Python for enterprise AI ecosystems

Java offers better energy efficiency, stable dependency management, and robust enterprise integrations compared to Python.

#3 about 3 min

Building AI interactions in Java with LangChain4j

LangChain4j simplifies connecting to large language models, applying prompt templates, and managing context memory.

#4 about 2 min

Enabling function calling and tooling for models

Tooling mechanisms allow language models to dynamically execute tasks like checking weather or sending emails.

#5 about 3 min

Demonstrating a naive theme park chatbot application

A basic AI application demonstrates using provided documentation and location context to answer specific user queries.

#6 about 4 min

Scaling challenges in basic context injection workflows

Overloading a single model with excessive context or relying on basic routing structures increases costs and limits maintainability.

#7 about 2 min

Managing complex workflows with LangGraph4j orchestration

Orchestrating multiple models and persistent checkpoints facilitates complex event-driven capabilities and human-in-the-loop interactions.

#8 about 3 min

Understanding graphs with nodes, edges, and state

Graph-based architectures represent execution steps as distinct nodes connected by conditional edges and sharing a global map holding iteration state.

#9 about 8 min

Executing stateful logic and pausing for human input

Defining checkpoints allows developers to pause node execution until a user manually injects necessary context into the shared state.

#10 about 7 min

Categorizing and processing emails using an AI graph

Specialized models collaborate across multiple conditional nodes to categorize emails, generate reliable replies, or perform web searches.

Matching moments

3:25 min

Constructing scalable AI solutions using LangChain and LangGraph

Julián Duque Julián Duque · World Congress 2025

2:54 min

Utilizing Java frameworks to interface with AI models

Mary Grygleski Mary Grygleski · LIVE

2:35 min

Evaluating frameworks and abstraction levels for building AI agents

Ahmad Adel Ahmad Adel · Europe 2026 Virtual

3:36 min

Overview of generative AI and the presentation agenda

juarezjunior juarezjunior · World Congress 2024

2:57 min

Comparing popular Java frameworks for AI integration

Timo Salm Timo Salm · World Congress 2025

44 sec

Building multi-agent systems using popular frameworks and libraries

Mary Grygleski Mary Grygleski · Europe 2026 Virtual

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