I want to share my experience in the cloud, AI, and agents development areas.
This blog post details the author's exploration of IBM Bob while building an MCP server for Astra DB. It emphasizes learning through experimentation in Code Mode, focusing on automation and iterative development. The author shares insights on prompt creation, workflow challenges, and the importance of documentation throughout the process, ultimately achieving a functional server setup.
In this post, I share my initial experiences with IBM Bob, an AI SDLC tool. By discuss setting it up with VS Code, its configurable modes, and key features. I show some details using IBM Bob to build a RAG system, highlighting its impressive support for planning, coding, and documentation, enhancing workflow efficiency. Introduction First... Continue Reading →
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.
This blog post outlines a practical example of setting up a custom component in Langflow to connect with an external weather API and import it into the watsonx Orchestrate Development Edition. The process emphasizes learning through experimentation rather than achieving a flawless solution, highlighting the potential of Langflow and watsonx Orchestrate for AI development.
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.
The post outlines using the Evaluation Framework in watsonx Orchestrate ADK to verify AI Agent behavior through a practical example: Galaxium Travels, a fictional booking system. It details setting up the environment, defining user Stories, generating synthetic Test Cases, and running evaluations, crucial for ensuring AI reliability and transparency.
This post outlines the process of setting up a local watsonx Orchestrate server and invoking a simple agent via REST API using Python. It covers environment setup, Bearer token retrieval, agent ID listing, and code execution.
This post guides users through building an AI agent locally using the watsonx Orchestrate Agent Development Kit (ADK), exporting it from their local setup, and importing it into a remote instance on IBM Cloud. The process enhances local development while ensuring efficient production deployment.
This post shows how to use Langflow with watsonx.ai and a custom component for a “Temperature Service” that fetches and ranks live city temperatures. It covers installation, flow setup, agent prompting, tool integration, and interactive testing. Langflow’s visual design, MCP support, and extensibility offer rapid prototyping; future focus includes DevOps and version control.
The blog post outlines the process of setting up the Agent Developer Kit (ADK) to build and run AI agents locally using WatsonX Orchestrate Developer Edition. It involves setting up prerequisites, installing the necessary software, and loading an example agent—optional integration with Langfuse for observability.
Thomas Suedbroecker's Blog 