I want to share my experience in the cloud, AI, and agents development areas.
The post explains why traditional Retrieval-Augmented Generation (RAG) approaches no longer scale and how modern architectures, including GraphRAG, address these limitations. It highlights why data quality, metadata, and disciplined system design matter more than models or frameworks, and provides a practical foundation for building robust RAG systems, illustrated with IBM technologies but applicable far beyond them.
The post discusses integrating Knowledge Graphs with Retrieval-Augmented Generation (RAG), specifically using Neo4j and LangGraph. It outlines an example setup where extracted document data forms a structured graph for querying. The system enables natural question-and-answer interactions through AI, enhancing information retrieval with graph relationships and embeddings.
This post explains how to create, test, and integrate AI support agents using IBM's watsonx.ai and watsonx Orchestrate. It describes an example to integrate a Specialist Support Agent for DB2, into multi-agent orchestration, and highlights best practices for creating efficient agent workflows and accurate responses while anticipating potential complexities.
Thomas Suedbroecker's Blog 