Alex Soto

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.

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

Accessing the presentation slides and demo code

A QR code is provided to download the presentation slides, which contain links to all the demonstration code shown in the talk.

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Matching moments

Understanding core AI concepts like LLMs, agents, and MCPs
08:25 MIN

Understanding core AI concepts like LLMs, agents, and MCPs

aa

Building complex agents with LangGraph
14:14 MIN

Building complex agents with LangGraph

Building AI Applications with LangChain and Node.js

Understanding the core components of an AI agent
00:05 MIN

Understanding the core components of an AI agent

Agentic AI - From Theory to Practice: Developing Multi-Agent AI Systems on Azure

Exploring frameworks for building agentic AI applications in Java
05:24 MIN

Exploring frameworks for building agentic AI applications in Java

Supercharge Agentic AI Apps: A DevEx-Driven Approach to Cloud-Native Scaffolding

Integrating generative AI into Java applications with LangChain4j
00:03 MIN

Integrating generative AI into Java applications with LangChain4j

Infusing Generative AI in your Java Apps with LangChain4j

Understanding LangChain4j for Java AI applications
02:52 MIN

Understanding LangChain4j for Java AI applications

Create AI-Infused Java Apps with LangChain4j

Simplifying GenAI development with the LangChain4J framework
09:53 MIN

Simplifying GenAI development with the LangChain4J framework

Langchain4J - An Introduction for Impatient Developers

Defining the agent's behavior with a Java interface
13:27 MIN

Defining the agent's behavior with a Java interface

Supercharge Agentic AI Apps: A DevEx-Driven Approach to Cloud-Native Scaffolding

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