Part 1 - Why I Picked LangChain4j Over Spring AI

Distributed sagas are hard enough without AI. You're already dealing with compensating transactions, Kafka topics, state machines, and rollback chains across 5 microservices. Adding an AI layer on top sounds like a recipe for more complexity. But that's exactly what this series covers: where AI actually helps in a saga-based architecture, and how to wire it up without making the system more fragile. The AI layer auto-diagnoses failures, dynamically reorders saga steps based on real failure data, and lets developers query the entire system in natural language. This first post covers the foundation: why I went with LangChain4j as the Java SDK, the core concepts you need, and how to get a working agent running. Why LangChain4j If you're building AI-powered applications in Java, you're choosing between three options: Python's LangChain (separate stack), Spring AI (native Spring), or LangChain4j (standalone Java library). Here's how they compare on the things that matter for production: Lan