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

Is your Java AI application turning into spaghetti code? Learn how to orchestrate complex, multi-step agents as stateful graphs for more robust and maintainable enterprise solutions.

#1about 1 minute

Why Java is a strong choice for enterprise AI development

Java offers advantages over Python in enterprise settings due to performance, dependency management, and its mature ecosystem for large corporations.

#2about 4 minutes

An overview of the LangChain4j framework for Java

LangChain4j simplifies AI development in Java by providing abstractions for models, memory, prompt templates, and function calling via AI services.

#3about 3 minutes

Building a simple theme park chatbot with LangChain4j

A practical demonstration shows how to build a theme park chatbot that answers questions using document retrieval and function calls.

#4about 3 minutes

Identifying the problems with monolithic AI agents

Simple agent architectures that send all context to the model at once lead to higher costs, slower responses, and increased hallucinations.

#5about 3 minutes

Using LangGraph4j to create stateful, cyclical agent graphs

LangGraph4j provides a framework for defining complex, multi-step agentic workflows as stateful graphs with nodes, edges, and a shared state.

#6about 5 minutes

Demonstrating a non-AI graph with conditional logic

A code walkthrough shows how to define a graph's state, create nodes for functions, and use conditional edges to route the execution flow.

#7about 3 minutes

Implementing human-in-the-loop workflows with checkpoints

LangGraph4j supports pausing a graph's execution at a checkpoint, allowing for human review or input before resuming the process.

#8about 6 minutes

Designing an advanced AI agent for customer service emails

A multi-step graph demonstrates an AI agent that categorizes emails, searches the web, drafts responses, and uses guardrails to verify its own output.

#9about 1 minute