Orchestrating Agentic and Multimodal AI Pipelines with Apache Camel

A deep dive into building production-grade agentic and multimodal AI pipelines using Apache Camel as an orchestration control plane and LangChain4j as the agent runtime. The article walks through a real-world support ticket triage system that combines LLM-based reasoning, RAG with Qdrant, and image classification via TensorFlow Serving with ResNet50. Key architectural insights include separating LLM reasoning from execution control, treating AI components as unreliable dependencies requiring circuit breakers and retries, and building multimodal systems without multimodal models by converting images to structured signals before passing them to the LLM. Tradeoffs of using Java-based Camel over Python-native frameworks are honestly discussed.