Image description(Originally Published on My Substack)

Hey Java devs! 👋

Have you been hearing the buzz around "agentic AI" and wondered what it actually means for you? It might sound like sci-fi, but trust me, it's a very real and surprisingly accessible way to build powerful AI-powered applications, especially with your existing Java skills.

This post, originally published on my MyFear Substack, dives into the core concepts of agentic AI and how you can start leveraging it using modern Java tools like Quarkus and LangChain4j.

Decoding the Agentic AI Lexicon

Let's get some key terms straight. The AI landscape is evolving rapidly, so these are some fundamental definitions to get us started:

  • Agent: Think of it as an LLM-powered program with a mission. It can reason and use tools to achieve a specific goal.
  • LLM (Large Language Model): The brains of the operation – models like GPT-4, Mistral, or LLaMA that can generate text, answer questions, and decide the next course of action.
  • Tool / Function Calling: This is how the LLM does things – calling Java methods, hitting APIs, or querying databases.
  • RAG (Retrieval-Augmented Generation): Enhancing the LLM's knowledge by fetching external data and feeding it into the context.
  • MCP (Model Context Protocol): Imagine this as the session manager for your LLM interactions, structuring and tracking conversations over time.
  • LangChain4j: Your Java-native toolkit for building AI applications with agents, tools, chains, and memory.
  • Quarkus: A supersonic, cloud-native Java framework – perfect for building fast and efficient AI-infused applications.

What's the Magic of "Agentic"?

Instead of just passively responding to prompts, agentic AI empowers language models to:

  • Understand the task at hand.
  • Strategically choose the right "tool" for the job.
  • Execute actions by calling external functions or APIs.
  • Remember past interactions and context.
  • Adapt their approach if things don't go as planned.

Think of a travel assistant again. A regular LLM might give a generic response to "Book me a hotel in Rome next weekend." An agentic AI, however, could:

  • Use a calendar tool to figure out the exact dates for "next weekend."
  • Use a hotel search API to find available options.
  • Select a hotel and use a booking service to make a reservation.
  • Provide you with a confirmation and real booking details.

The key difference? Action and reasoning, not just conversation.

Getting Hands-On: The Java Agentic AI Stack

For us Java developers, a powerful stack for building agentic AI looks like this:

  • Quarkus: The speedy runtime for our application.
  • LangChain4j: To define our agents and their tools.
  • MCP: For structured, traceable LLM interactions.
  • Ollama (for local models) or cloud-hosted models (like OpenAI or Mistral) for the LLM itself.

Tools in Java: Simple as Annotations

With Quarkus and LangChain4j, turning your existing Java methods into powerful tools for your agents is incredibly straightforward. Just annotate any public method with @Tool:

@ApplicationScoped
public class HotelBookingTool {

    @Tool("book hotel in city")
    public String bookHotel(String city, String date) {
        return "Hotel booked in " + city + " for " + date;
    }
}

No complex API layers or extra glue code needed!

Building Your AI Service with Quarkus

Your AiService acts as the central hub connecting your application to the LLM. It neatly abstracts the LLM details within a single interface. Register your tools like this:

@RegisterAiService(tools = HotelBookingTool.class)
public interface MyAiService {
    // Your agent methods here
}

Exposing Your Agent via REST

Want to make your agent accessible? Quarkus makes it easy to expose it through a REST endpoint:

@Path("/ask")
@ApplicationScoped
public class AgentResource {

    @Inject
    MyAiService agent;

    @POST
    @Consumes(MediaType.TEXT_PLAIN)
    @Produces(MediaType.TEXT_PLAIN)
    public String ask(String question) {
        return agent.chat(question);
    }
}

This instantly turns your agent into a deployable microservice. (Note: This is a simplified example – check the official documentation for more in-depth implementations!)

Leveraging the Model Context Protocol (MCP)

The Model Context Protocol (MCP) is crucial for building robust agentic applications. It allows you to capture the entire interaction flow, enabling:

  • Persistent conversation history across requests.
  • Easy debugging and tracing of tool calls.
  • The ability to rewind or restore sessions.

Integrating an MCP server in Quarkus is quite simple. You can find more details on how to do this in the [invalid URL removed] and learn how to connect it to your application in the [invalid URL removed].

Creating Intelligent Agents with Multiple Tools

To build truly powerful agent-based systems, you'll likely need more than one tool, along with well-defined prompts. Here's a glimpse using the @ToolBox annotation:

@RegisterAiService(modelName = "openai")
public interface WeatherForecastAgent {

    @SystemMessage("""
        You are a meteorologist answering weather questions.
        Keep responses brief and accurate.
        """)
    @ToolBox({CityExtractorAgent.class, WeatherForecastService.class})
    String provideForecast(String userQuery);
}

This setup allows the LLM to reason about the user's query and strategically use the available city and weather services. You can find more information on defining agents in the [invalid URL removed].

Addressing Common Questions

  • "Why not just call Java methods directly?" Agentic AI provides adaptability. It's a reasoning layer that decides how to use your tools for open-ended requests, offering more flexibility than hardcoded logic.
  • "Isn't this just RPA or scripting?" Not really. Traditional scripting follows fixed workflows. Agentic AI dynamically chooses tools and adapts based on context, user input, and memory.
  • "How is this different from a chatbot?" A basic chatbot follows conversational patterns. An agent executes tasks, orchestrates tools, and returns concrete results, acting more like a digital teammate.

Real-World Use Cases: The Potential is Huge

We're just scratching the surface of what agentic AI can do, but here are some exciting possibilities:

  1. Code Review Agent: Analyze PR diffs, identify bugs and anti-patterns using a CodeAnalyzerTool, and even file GitHub issues.
  2. Sales Coach Agent: Evaluate call transcripts, suggest next steps, and log customer insights into your CRM.
  3. DevOps Agent: Answer questions like "What pods are failing in staging?" by querying your Kubernetes API and providing potential root causes.
  4. WildFly Agent: Interact with your WildFly server in natural language for deployment management and troubleshooting.

All of this becomes possible by combining Quarkus' integration capabilities with LangChain4j's reasoning framework and MCP's transparency.

Dive Deeper: More Resources

The Quarkus team has some excellent resources to help you on your agentic AI journey:

  • Part 1 introduces agents, tools, and LangChain4j basics.
  • Part 2 dives into chaining agents together (e.g., a weather bot using a geocoding bot).
  • Quarkus + MCP explains how to structure durable, debuggable interactions across sessions.
  • Building your own MCP Server shows you how to register tool servers that LLMs can call over HTTP or stdio.

The Future is Agentic, and Java Devs are Ready

Agentic AI is rapidly moving from buzzword to practical architecture. With LangChain4j and Quarkus, you don't need to learn new languages or rely solely on cloud vendors to get started.

You already possess the core skills in Java to build robust business logic and APIs. Now, you can empower your applications with structured reasoning, tool access, and memory, all while leveraging the wealth of data in your customers' existing systems.

Start small. Experiment with adding an agent to your helpdesk, creating a data summarizer, or building a DevOps assistant. The potential for scaling these intelligent agents is immense.

What are your thoughts on agentic AI and its potential in the Java ecosystem? Let's discuss in the comments below! 👇