Orchestrating Complex AI Agents with KahnQueue and Temporal

If you want to learn Spring AI, I highly recommend this presentation "Integrating AI into Your Java Applications: Spring AI Deep Dive with RAG" by Christian Tzolov https://lnkd.in/gBM8S_jr This talk covers, foundational concepts of Spring AI , advanced concepts like Agentic Patterns, AI Protocols, etc. #Java #SpringAI #SpringBoot #AgenticPatterns

A technique termed Retrieval Augmented Generation (RAG) has emerged to address the challenge of incorporating relevant data into prompts for accurate AI model responses.Spring AI greatly simplifies code you need to write to support RAG pipelines. #SpringAI #Java

Did you miss AI4J™ - The Intelligent Java Conference? Watch on demand! Get all the info on your own schedule. Grab some popcorn 🍿 and a notepad 📒 and dive in. 🗝️ Key Takeaways: • Moving from “cool demos” to trustworthy, production-grade AI requires deliberate context engineering—not just better models. • The future of Java apps isn’t just AI-enabled—it’s AI-orchestrated, where agents actively interact with your systems. • Advances in tooling and efficiency are making predictive AI more scalable and production-ready in Java. Winning organizations will combine GenAI + predictive AI, not replace one with the other. 💻 Watch Now: https://bit.ly/41y3zTt #AI4J2026 #JavaAI

This is required viewing for those struggling to adapt enterprise AI solutions in their enterprise. Move from project to performance within a familiar architecture.

Continuing from my previous post on AI + Java… A common thought after that is: 👉 “This looks interesting… but how do I actually use it in my current project?” Let’s break it down simply 👇 🧠 Most Spring Boot services today follow this flow: Client → Controller → Service → DB / APIs → Response Now, to integrate AI… 👉 You don’t need a new system 👉 You extend your existing service 💡 Just one addition: ➡️ Add an LLM call inside your service layer 📊 Updated flow: Client → Controller → Service (AI Orchestrator) → Fetch Data → Build Context → Call LLM → Return smarter response 💡 Key takeaway: AI is added inside the service layer NOT as a separate system 📌 Example use cases: Search API → smarter results Support API → auto replies Recommendation API → personalization Most systems don’t need a redesign. Just a small extension. 👉 Curious: Which API in your current project could be enhanced with AI? #Java #AI #SpringBoot #SoftwareArchitecture #BackendDevelopment

💡 Modern Java has changed — most people just haven’t noticed yet. With the Vector API and Foreign Function & Memory (FFM) API, we’re no longer talking about “Java trying to keep up with AI”… We’re talking about Java becoming a serious platform for Enterprise AI. 📌 That means: • Running AI where your production systems already live • Eliminating unnecessary layers and glue code • Getting real performance on the JVM • Building AI systems that actually scale in enterprise environments This isn’t about replacing Python. It’s about removing excuses. If you’re still assuming Java can’t handle AI workloads — you’re solving yesterday’s problems. The shift is already happening. 👉 If you’re building enterprise systems with Java, it’s time to rethink what’s possible. #Java #AI #EnterpriseAI #MachineLearning #JVM #SoftwareEngineering #TechShift https://lnkd.in/eQ9iGGaX

AI-native IDEs are fundamentally changing how software engineering teams operate. But writing code faster is only half the battle. If your team is still bottlenecked by lengthy Java build and restart times, you're losing the efficiency AI gives you. We break down the pros, cons, and use cases of the Top 4 AI IDEs (Cursor, Windsurf, Kiro, Antigravity) for Java. Pro Tip: Combine these tools with JRebel for zero-latency hot reloads. Read the full comparison below and reach out to us to supercharge your DevSecOps pipeline!https://lnkd.in/gh5zKxmr #Java #AI #DevSecOps #Productivity #SoftwareEngineering #Dragonsoft

Loved the conversations coming out of AI4J. From context engineering to predictive AI and functional code, check out this recap to learn how Java continues to play a major role in modern AI systems. #Java #AI4J #AI #Engineering #Developers

Exploring how Java + AI can shape the future of enterprise applications. My key takeaway: AI isn’t replacing Java — it’s making applications smarter. Sharing a simple visual that captures this thinking 👇 Curious how others see this space evolving. #Java #AI #Innovation #SoftwareDevelopment #Curiosity

Excited to see Java experts breaking down what actually matters in AI engineering — structured data, deterministic AI, and smarter agent design. If you missed AI4J, this recap is worth a read. #Java #AI4J #AI #Engineering #Developers