Java AI IDEs: Pros Cons and Use Cases for DevSecOps

Gen AI in production is ~10% model and 90% integration. That’s where Java wins. While everyone is focused on Python for AI, the enterprise world is moving differently. Spring AI reached GA in 2025, and even engineers from Netflix have contributed to its evolution. That’s a strong signal: AI isn’t replacing enterprise stacks — it’s being embedded into them. Here are 3 JVM frameworks quietly shaping this shift: --- Spring AI (the Spring way of doing AI) The Spring philosophy applied to AI: → Provider abstraction (OpenAI, Mistral, Gemini…) → RAG out-of-the-box → Function calling → Embeddings management If you know Spring Boot, you already know how to build AI features. AI stops being “something separate” and becomes just another bean in your application. --- Embabel (agentic AI for DDD) Where Spring AI connects models, Embabel builds agents. Agents that operate directly on: → Your Java entities → Your services → Your business rules This is not generic agent orchestration. This is agentic AI aligned with your domain model. --- Koog (JetBrains) A modern multi-agent framework from JetBrains. → Built in Kotlin, fully JVM compatible → Designed for orchestrating specialized agents → Focused on complex workflows and collaboration Agent systems, but with JVM rigor and structure. --- Why Java over Python for production? → Strong typing = fewer surprises in prod → Enterprise ecosystem = no need to rewrite everything → JVM = performance, scalability, maturity → Built-in alignment with compliance (GDPR, SOX, PCI-DSS) --- Python dominates model experimentation. Java dominates enterprise integration. The AI that creates real business value is not the one with the best prompt. It’s the one that: → integrates with existing systems → respects constraints → scales reliably Spring AI, Embabel, and Koog are enabling that next step. #Java #SpringAI #Embabel #Koog #EnterpriseAI #AgenticAI #LLM #SpringBoot #JVM #SoftwareArchitecture #AIEngineering

Every Java dev knows the feeling of trying to learn faster… and somehow ending up even more overwhelmed. AI was supposed to fix that, right? This video goes straight into that strange moment when AI gives you more answers, more options, more roadmaps… and way less clarity. It shows the hidden problem many developers don’t realize they’re stuck in. If you’ve been jumping between topics, tutorials, and AI prompts and still not moving toward better opportunities, this will make you pause. Weekly Live → https://bit.ly/48ophNZ

I’ve been experimenting with Temporal to orchestrate complex AI agents, and I hit a specific challenge: scheduling sub-agents with deep inter-dependencies. I needed a way to handle orchestrating agents where Agent C can’t start until Agents A and B have finished. Additionally, A and B should run concurrently if possible. I looked at how build systems have handled this for decades. That led me to create KahnQueue, a Java async scheduler fueled by Kahn’s algorithm. It treats agent tasks like a dependency graph. Instead of hard-coding the sequence, you define the dependencies and let the scheduler resolve the execution order. The scheduler logic is here for anyone looking at dependency-based execution: https://lnkd.in/g7nbZeTA #SoftwareArchitecture #GenerativeAI #AgenticWorkflows #LLMOps #Temporal

How do you actually start building Java applications with AI the right way? 🤖 Everyone is talking about AI, but integrating it properly into the Java ecosystem requires a solid strategy. Tomorrow on Asking a Java Champion, I’m joined by Simon Martinelli to discuss exactly how to begin. We’re moving past the hype to look at real implementation and best practices for developers. 🔔 Full episode drops tomorrow! Catch it here: youtube.com/@lgsreal #Java #AI #SoftwareEngineering #JavaChampions #AJC #Backend #Innovation #SimonMartinelli

Java has a place in the enterprise AI conversation. Embabel is one more reason why. If you've spent years building Spring Boot systems, you already know the hardest part of enterprise AI: the business, the domain, the workflows, the edge cases, the compliance rules. That knowledge doesn't come from an LLM; it comes from being close to the problem for a long time. That's what makes Embabel interesting. Embabel is a JVM agent framework built on Spring AI, created by Rod Johnson. It lets you model agents as Goals, Actions and Conditions over your own domain objects. Strong typing, Spring component model, testable from day one. And then there is the planner: Goal-Oriented Action Planning (GOAP). An AI technique from game development. You define the goal and the available actions, and the framework works out the sequence. If something changes, it replans. Same inputs, same plan. Deterministic and explainable. That matters a lot when you need to audit what an agent did and why. I haven't built anything with Embabel yet. But the direction feels right: bringing agents closer to the code and the people who already know the business. #embabel #ai #java #springboot

Recently attended a session on Prompt-Driven Development: Building Secure Enterprise Java Apps with AI. The webinar highlighted how AI is transforming software development—from writing better code to improving security and efficiency in enterprise applications. Always interesting to see how rapidly development is evolving with AI integration. Looking forward to exploring more in this space 🚀 #AI #Java #Innovation #SoftwareEngineering #Nest #Java #SpringBoot #AI #SoftwareEngineering #BackendDevelopment #PromptEngineering

Most Java developers are using AI like a smarter Stack Overflow. That’s not where things are headed. 🚫 We’ve already moved from: 💻 “AI helps me write code” ➡️ to ⚙️ “AI helps me ship systems” If you're still thinking in terms of prompts & context, you're missing the bigger shift. The real leverage comes from: 🔗 Orchestrating workflows 🧩 Connecting tools 🤖 Letting AI execute Think beyond: Spring Boot APIs Manual integrations Endless debugging loops Start thinking: AI + CI/CD pipelines AI + automated testing AI + system-level execution This is where backend engineering is going. And it’s happening faster than most teams realize. ⚡ #AI #BackendDevelopment #Java #SoftwareEngineering #AIAgents #TechTrends

Every Java developer is watching the AI wave. Most don't know you can ride it without leaving Spring Boot.Save this carousel. The developer who starts with Spring AI in April will be leading the AI project by Q3. #SpringAI #JavaDeveloper #SpringBoot #AIForDevelopers #GenerativeAI

🚀 Building a Coding Agent with Spring AI Recently, I came across a great YouTube tutorial by Dan Vega titled: “Build a Coding Agent Like Claude Code with Spring AI” It was one of those demos that looks simple on the surface but teaches a very powerful pattern. 🤖 What did I learn? In just ~60 lines of Java, you can build a CLI-based AI coding assistant using Spring Boot + Spring AI that can: ✅ Read files from a codebase ✅ Search code using grep & glob ✅ Run shell commands ✅ Maintain conversational memory ✅ Reason autonomously about which tool to use and when You point the agent at a project and ask questions like: “What does the main application class do?” “Find all TODOs in the codebase” “Run the tests and tell me what failed” The LLM (Claude) decides which tools to call, chains them together, and responds intelligently — all from your terminal. 🧠 Why this is interesting (beyond the demo) This project isn’t just about a “coding assistant”. It demonstrates a powerful architectural pattern: An LLM + Tools + Memory + Conversational Loop And this same pattern can be applied to: 🔧 DevOps & Infrastructure assistants 📊 Log analysis & debugging 🗄️ Database exploration via JDBC tools 📚 Internal documentation search 🔐 Security auditing (grep + tools like semgrep/trivy) 🧪 Test generation and execution 🧭 Exploring large or legacy codebases 🧩 Tech Stack Used Spring Boot 4.0.2 Spring AI 2.0.0-M2 Spring AI Agent Utils Claude (Anthropic) Java 25 What I loved most 👉 model‑agnostic design. Thanks to Spring AI abstractions, switching from Anthropic to OpenAI, Ollama, or another provider requires config changes only, not code changes. 🌱 Key takeaway AI agents don’t need massive frameworks or thousands of lines of code. With the right abstractions, you can build useful, tool‑aware agents that work directly with real systems — not just chat windows. Big thanks to Dan Vega and the Spring AI community for such a clear and practical example 🙌 📌 I’m sharing this as part of my learning spring ai journey. If you’re experimenting with Spring AI, AI agents, or developer productivity tools, I’d love to hear your thoughts! #SpringAI #Java #AIEngineering #LLM #Claude #DeveloperTools #LearnInPublic #SpringBoot #Agents

🤖 𝗦𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: 𝗘𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗦𝗽𝗿𝗶𝗻𝗴 𝗔𝗜 𝗮𝗻𝗱 𝗚𝗿𝗼𝗾 🚀 I’ve been spending time lately digging into the new 𝗦𝗽𝗿𝗶𝗻𝗴 𝗔𝗜 ecosystem. My goal was to see how easily 𝗝𝗮𝘃𝗮 𝗮𝗻𝗱 𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁 can now integrate with advanced AI models. To test it out, I built a tool called 𝗩𝗶𝗴𝗶𝗹𝗮𝗻𝘁 𝗔𝗜. 𝘞𝘩𝘢𝘵 𝘪𝘵 𝘥𝘰𝘦𝘴:  • It analyzes your Java code to find deep-seated 𝘭𝘰𝘨𝘪𝘤𝘢𝘭 𝘣𝘶𝘨𝘴.  • It suggests improvements for 𝘤𝘰𝘥𝘦 𝘴𝘦𝘤𝘶𝘳𝘪𝘵𝘺 𝘢𝘯𝘥 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦. This project was a fantastic way to explore: ✅ 𝗚𝗿𝗼𝗾 𝗔𝗣𝗜: Leveraged the ultra-fast Llama 3.1 model for intelligent code analysis. ✅ 𝗦𝗽𝗿𝗶𝗻𝗴 𝗔𝗜: Saw firsthand how simplified it is to connect LLMs to your existing Java stack. ✅ 𝗟𝗼𝗰𝗮𝗹 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀: Implemented local vector processing (ONNX) to keep certain operations fast and free. If you’re a Java developer looking to get started with AI, I highly recommend checking out Spring AI. It makes the transition seamless. Check out the code on GitHub to see how I built it!  𝗚𝗶𝘁𝗵𝘂𝗯: https://lnkd.in/gyn89zWN #SpringAI #Java #Groq #Llama3 #SpringBoot #SoftwareEngineering #AIDevelopment #LearningByDoing #VigilantAI