You’ve likely heard it everywhere lately: “This is the year of Agentic AI”!! Well, then why not roll up your sleeves and try creating your own Java-based Agentic AI app? As a Java developer, you are absolutely equipped to take on the challenge! In this session, you’ll explore a variety of Agentic AI capabilities with Java.

We’ll start by creating a simple AI client to interact with an LLM. We’ll then explore how we can make this app “agentic” by adding a variety of agentic capabilities, such as local function calling, MCP, Agent2Agent, and more. In addition, we’ll try out different techniques and patterns to get your LLM leveled up to leverage these Agentic capabilities. We’ll also attempt to show that agents are in fact not always needed, and show alternative patterns to accomplish AI tasks.

Come to this session to learn how to build Agentic AI applications with Java from the experts and engineers actively working on Quarkus AI and LangChain4j. Not a Quarkus developer but rather a Spring, or other stack developer? No worries! The concepts translate across the Java spectrum.

You’ve likely heard it everywhere lately: “This is the year of Agentic AI”!! Well, then why not roll up your sleeves and try creating your own Java-based Agentic AI app? As a Java developer, you are absolutely equipped to take on the challenge! In this session, you’ll explore a variety of Agentic AI capabilities with Java.

We’ll start by creating a simple AI client to interact with an LLM. We’ll then explore how we can make this app “agentic” by adding a variety of agentic capabilities, such as local function calling, MCP, Agent2Agent, and more. In addition, we’ll try out different techniques and patterns to get your LLM leveled up to leverage these Agentic capabilities. We’ll also attempt to show that agents are in fact not always needed, and show alternative patterns to accomplish AI tasks.

Come to this session to learn how to build Agentic AI applications with Java from the experts and engineers actively working on Quarkus AI and LangChain4j. Not a Quarkus developer but rather a Spring, or other stack developer? No worries! The concepts translate across the Java spectrum.

Generative AI has taken the world by storm over the last year, and it seems like every executive leader out there is telling us “regular” application developers to “add AI” to our applications. Does that mean we need to drop everything we’ve built and become data scientists instead now?

Fortunately, we can infuse AI models built by actual AI experts into our applications without too much pain. Thanks to the ease of use and superb developer experience of Quarkus (a Java stack) and the nice AI integration capabilities that the LangChain4j libraries offer, it becomes trivial to start working with AI and make your stakeholders happy :)

In this session, you’ll explore how with a few lines of code, you can add a variety of AI capabilities: prompting, chaining, and preserving state; agents and function-calling; enriching your AI model’s knowledge with your own documents using retrieval augmented generation (RAG); In addition, we'll add observability and fault tolerance to the AI integration.

What about "Agentic AI" and "MCP" clients and servers? No worries, we'll show you how to create those as well!

Come to this session to learn how to build Agentic AI-infused applications with Java, LangChain4j and Quarkus.

PS. Not a Java developer? Allow us to blow your mind and show you that Quarkus is not your grandma's Java.

Generative AI has taken the world by storm over the last year, and it seems like every executive leader out there is telling us “regular” application developers to “add AI” to our applications. Does that mean we need to drop everything we’ve built and become data scientists instead now?

Fortunately, we can infuse AI models built by actual AI experts into our applications without too much pain. Thanks to the ease of use and superb developer experience of Quarkus (a Java stack) and the nice AI integration capabilities that the LangChain4j libraries offer, it becomes trivial to start working with AI and make your stakeholders happy :)

In this session, you’ll explore how with a few lines of code, you can add a variety of AI capabilities: prompting, chaining, and preserving state; agents and function-calling; enriching your AI model’s knowledge with your own documents using retrieval augmented generation (RAG); In addition, we'll add observability and fault tolerance to the AI integration.

What about "Agentic AI" and "MCP" clients and servers? No worries, we'll show you how to create those as well!

Come to this session to learn how to build Agentic AI-infused applications with Java, LangChain4j and Quarkus.

PS. Not a Java developer? Allow us to blow your mind and show you that Quarkus is not your grandma's Java.

Generative AI has taken the world by storm over the last year, and it seems like every executive leader out there is telling us “regular” application developers to “add AI” to our applications. Does that mean we need to drop everything we’ve built and become data scientists instead now?

Fortunately, we can infuse AI models built by actual AI experts into our applications without too much pain. Thanks to the ease of use and superb developer experience of Quarkus (a Java stack) and the nice AI integration capabilities that the LangChain4j libraries offer, it becomes trivial to start working with AI and make your stakeholders happy :)

In this session, you’ll explore how with a few lines of code, you can add a variety of AI capabilities: prompting, chaining, and preserving state; agents and function-calling; enriching your AI model’s knowledge with your own documents using retrieval augmented generation (RAG); In addition, we'll add observability and fault tolerance to the AI integration.

What about "Agentic AI" and "MCP" clients and servers? No worries, we'll show you how to create those as well!

Come to this session to learn how to build Agentic AI-infused applications with Java, LangChain4j and Quarkus.

PS. Not a Java developer? Allow us to blow your mind and show you that Quarkus is not your grandma's Java.