The Future of Java and AI: Coding in 2025

Expanding on the findings of "The State of Coding the Future with Java and AI" survey, this article focuses more on the unique perspective and potential for developers leveraging Quarkus for Java AI.

Software development is evolving rapidly, and Java remains a cornerstone for enterprise applications, especially as Artificial Intelligence (AI) reshapes the coding landscape. In 2025, Java developers are at the forefront of this transformation, harnessing AI tools and frameworks like Quarkus to build scalable, cloud-native, and intelligent applications. 

This article, inspired by insights from "The State of Coding the Future with Java and AI" survey, dives into the state of Java development, the role of AI, and why Quarkus is a game-changer for modern Java workloads.

Java’s Enduring Relevance

Java’s longevity stems from its portability, robustness, and vibrant ecosystem. With over 30 years of evolution, Java continues to power enterprise backends, cloud-native microservices, and now AI-infused applications. The release of Java 24 and the upcoming Java 25 bring performance optimizations, enhanced concurrency models, and better support for cloud deployments, making Java a top choice for developers.

The Java community is thriving, with events like Microsoft JDConf 2025 (April 9-10) showcasing advancements in cloud, AI, and frameworks like Quarkus. These gatherings highlight how Java adapts to modern demands, from microservices to serverless architectures.

AI: The New Frontier for Java Developers

AI is no longer a niche; it’s a core component of software development. Java developers are integrating AI to enhance applications with capabilities like natural language processing, predictive analytics, and intelligent automation. Tools like GitHub Copilot and Azure AI services are streamlining workflows, enabling developers to write cleaner code faster.

• AI-assisted coding: GitHub Copilot, integrated into IDEs like IntelliJ IDEA, suggests code snippets, automates testing, and debugs with remarkable accuracy.
• AI platform integration: Model as a Service allows Java developers to build a generative AI into applications, from chatbots to recommendation engines. Libraries like Quarkus Langchain4j integration simplify integration, enabling rapid prototyping.
• Custom AI agents: Developers can build autonomous agents for platforms like Model Context Protocol Java SKD using Java, automating tasks like scheduling or data analysis.

AI’s impact extends beyond coding assistance. It’s transforming how Java applications process data, optimize performance, and deliver personalized user experiences.

Quarkus: Infusing AI into Java Applications

LangChain4j, a Java port of the popular LangChain framework, abstracts away the complexity of working with large language models (LLMs). It supports multiple LLM providers like OpenAI, Azure OpenAI, and Cohere, as well as vector databases like Qdrant and Redis.

With the Quarkus LangChain4j extension, developers can:

• Implement Retrieval-Augmented Generation (RAG) to build intelligent search/chat experiences.
• Use document loaders and embedding models for vector-based retrieval.
• Compose AI pipelines entirely in Java, leveraging Quarkus’s dependency injection and test frameworks.

This allows Java developers to go from idea to production AI experience without needing to leave their comfort zone.

Quarkus for Agentic AI

Quarkus, combined with the MCP (Model Catalog Protocol) extension, empowers Java developers to build intelligent applications that can easily consume AI models as services. With the Quarkus MCP extension, developers can discover, bind, and invoke foundation models, such as those hosted on Azure Open AI, from within a native Java application using standardized APIs. 

This enables seamless integration of generative AI capabilities like text summarization, translation, and Q&A directly into Quarkus-based applications, streamlining the development of scalable, cloud-native AI solutions in pure Java.

Running Quarkus AI Apps on Azure

Azure provides a robust environment for deploying Quarkus-based AI apps. With support for:

• Azure OpenAI Service for scalable, enterprise-grade LLM access.
• Azure Container Apps and Azure Kubernetes Service (AKS) for Quarkus-native deployment targets.
• Qdrant on Azure for scalable vector similarity search.
• Microsoft Entra ID for secure authentication and authorization.

Java developers can build, test, and deploy intelligent applications efficiently, with full CI/CD pipelines and security best practices.

For example, a Java-based AI assistant using Quarkus and LangChain4j can be deployed in minutes to Azure Container Apps, accessing Azure OpenAI for LLMs and Qdrant for vector search — all through managed services.

Case Study: Azure RAG With Java, LangChain4j, and Quarkus

Microsoft has released a reference implementation demonstrating how to build a retrieval-augmented generation (RAG) solution using:

• Quarkus for the backend
• LangChain4j for AI integration
• Azure OpenAI and Qdrant for the LLM and vector store

It showcases document ingestion, semantic search, and intelligent response generation—all within a native Java stack. This not only proves feasibility but also opens a clear path for enterprise developers building similar applications.

The Road Ahead

Java’s future is bright, with AI and frameworks like Quarkus driving innovation. Developers are building applications that are faster, smarter, and more scalable than ever. By embracing tools like Quarkus, Azure AI, and GitHub Copilot, Java developers are not just keeping pace — they’re shaping the future of software.

More Resources

• Quarkus LangChain4j Deep Dive
• Quarkus LangChain4j workshop
• Getting started with your first AI application with Quarkus
• Survey on the State of Coding the Future with Java and AI