Java for Agentic AI Development with LangChain4j and Quarkus

In fact, it’s so effective you can make modernization a regular part of the software development lifecycle instead of a painful one-off project that gets postponed until systems are at breaking point, Borges argues. “That’s never happened, because the cost of modernization was so high and the return on investment was unpredictable at the very least.”

Powerful, scalable, reliable, cost efficient – and ready to be your next AI language? I'll admit I hadn't been thinking as much about Java for writing AI systems, just the inevitable data and workflow backend, but the frameworks are there. Plus AI coding tools are good enough for Java modernisation... It was interesting to talk to Bruno Borges and Julien Dubois about the state of coding assistants for Java; since it's the language that powers backend systems that enterprises are notably conservative about updating. If they could switch to being up to date by default, that continuous modernisation would mean a big change in software design lifecycles.

Really interesting perspective from Simon Ritter on how AI is reshaping the future of Java. His 2026 predictions highlight just how quickly enterprise development is evolving. Check it out here. #Java #TechPredictions #AI

☕ Why I Still Choose Java in the Age of AI In a world buzzing with Python and AI frameworks, some ask: "Is Java still relevant?" Absolutely. Here's why: 🔹 Enterprise Backbone – 90% of Fortune 500 companies run on Java. AI doesn't replace infrastructure; it enhances it. 🔹 AI Integration – From Deeplearning4j to Spring AI, Java is evolving. We're not just writing code; we're building intelligent systems. 🔹 Performance & Scale – When your AI model needs to serve millions of requests, Java's JVM optimization and concurrency handling become your superpower. 🔹 Write Once, Run Anywhere – Still true after 28 years. Deploy AI-enhanced applications anywhere. The mindset that matters: "Don't fear AI taking your job. Fear the developer who uses AI with Java better than you." Every NullPointerException taught me resilience. Every Stream API taught me elegance. Java isn't just syntax—it's a philosophy of robust engineering. To fellow Java developers: The language is mature, but our applications are becoming smarter. Keep learning. Keep building. The JVM is your launchpad, not your limit. #Java #AI #SoftwareEngineering #TechLeadership #Programming #DeveloperLife #JVM #ArtificialIntelligence #CodeNewbie #100DaysOfCode

Before I ever wrote a line of Python for an AI pipeline, I spent two and a half years debugging Java microservices at scale. At XRG Consulting, I worked on backend services for New Relic's observability platform. Spring Boot. Hibernate. REST APIs serving millions of daily queries across distributed microservices. That work didn't feel "AI" at all. It felt like plumbing. But looking back, it taught me almost everything I rely on now: How to design API contracts that don't break downstream consumers. How to think about latency, throughput, and reliability under real production load. How to trace a problem through layers of services when something fails at 2am. How to refactor legacy code without breaking the thing that's already working. When I moved into Python and started building LLM-powered workflows, I expected a steep learning curve on the AI side. And there was one. But the harder problems — keeping systems reliable, structuring async pipelines, making services observable — those were the same problems I'd been solving in Java for years. I think a lot of people underestimate how much traditional backend engineering matters in AI work. The LLM call is one line of code. Everything around it — the orchestration, the error handling, the data flow, the uptime — that's where the real engineering lives. I'm glad I didn't skip that chapter. #BackendEngineering #AIEngineering #Python #Java #SpringBoot #Microservices #SoftwareEngineering #CareerGrowth #BuildInPublic

Most Java devs are still treating AI as a feature. The ones winning in 2026 are treating it as an architect. Here's what that actually means in practice: Spring AI now lets you expose your existing business logic to AI models with minimal friction — using familiar annotations like @McpTool alongside @Service. That means your decade of enterprise Java knowledge isn't legacy baggage. It's your competitive edge. The mental model shift I keep seeing: ❌ Old thinking: "I'll add an AI endpoint to my Spring Boot app." ✅ New thinking: "My Spring Boot app IS the agent. It reasons, plans, and acts." At their core, AI agents are context orchestrators — continuously gathering information, querying models, evaluating outputs, and adapting their approach. Springio Spring AI's Advisor architecture is built exactly for this. Strong typing and null safety dramatically reduce runtime surprises in complex tool-calling chains and multi-agent coordination DZone — something Python stacks have been quietly struggling with at scale. The boring truth: the hardest part of agentic systems isn't the AI. It's reliability, observability, and security. That's where the JVM has always been unbeatable. Your stack didn't get disrupted. It got upgraded. #SpringAI #Java #AgenticAI #SpringBoot #BackendDevelopment #MCP

The multi-agent landscape in early 2025 looked like this: LangChain. CrewAI. AutoGen. OpenAI Swarm. All Python. All single-user. All missing what enterprises actually need to ship agents to production. Meanwhile, Java runs ~90% of enterprise backends. Spring Boot is the de-facto standard. But if you wanted to orchestrate AI agents in the JVM ecosystem, your options were essentially "write it yourself." We built SwarmAI to close that gap. Today we're open-sourcing it. → Built on Spring Boot 3.4 + Spring AI 1.0.4 GA → 8 orchestration patterns (Sequential, Parallel, Hierarchical, Iterative, Self-Improving, Swarm, Distributed, Composite) → Sealed Build → Compile → Execute lifecycle — catches errors before tokens get spent → Multi-tenancy, governance gates, budget enforcement, audit trails — architectural, not bolted on → Three RL policy engines (LinUCB contextual bandits, DQN with experience replay) for skill generation and stopping decisions, 4–12x better than Monte Carlo baselines → 38 built-in tools, MCP adapter, RAFT consensus for multi-node coordination → 1,400+ tests passing. Apache 2.0 for core. Full write-up with code, architecture, and benchmark methodology: https://lnkd.in/eWmmY2qq We'd love hard questions, issues, and PRs. #Java #SpringBoot #AIAgents #MultiAgent #OpenSource

Here’s a LinkedIn post focused on AI + LLM + Java — relevant, modern, and engaging: 🤖 Java + LLMs = More powerful than most people think A lot of AI/LLM discussions revolve around Python… But in real-world enterprise systems, Java is quietly becoming a strong player in AI integration. Here’s why: ✔️ Spring Boot + APIs → Perfect for exposing LLM-powered services ✔️ LangChain4j / custom integrations → Easy orchestration of LLM workflows ✔️ Microservices architecture → Clean separation between AI logic and core systems ✔️ Scalability + reliability → Java still wins in production environments 💡 What’s changing? We’re moving from: 👉 APIs serving data ➡️ To 👉 APIs serving intelligence Examples I’m seeing: 🔹 AI-powered search over enterprise data 🔹 LLM-driven automation using backend services 🔹 Agents interacting with APIs via structured schemas ⚠️ Challenges: ❗ Handling hallucinations ❗ Securing AI-accessible APIs ❗ Managing cost + latency 🚀 My takeaway: Java isn’t competing with AI — it’s becoming the backbone of production-grade AI systems. 👉 Are you integrating LLMs into your Java applications yet? #Java #AI #LLM #SpringBoot #Microservices #BackendDevelopment #TechTrends

My last blog post was about Jib, Google's daemonless Java image builder. That was July 2018. Anyone still remember Jib? Eight years of silence, two O'Reilly books later (Kubernetes Patterns and Generative AI on Kubernetes), and the tech world looks nothing like it did back then. So what breaks that kind of silence? Working with AI coding agents and context engineering has been piling up insights faster than any book cycle can handle. Rather than sitting on those ideas for another two years, I'd rather share them while they're still fresh. The blog is back, starting with why it went quiet, what changed, and where things are heading next. https://lnkd.in/dgiyEWu5 #Kubernetes #ContextEngineering #AIEngineering