I want to share my experience in the cloud, AI, and agents development areas.
AI systems rely heavily on data quality, which is often overlooked despite modern technical architectures. Issues like outdated, incomplete, or misaligned data can undermine system reliability, regardless of the sophistication of the components. Effective AI requires both high-quality data and solid technical infrastructure to meet user expectations and ensure trust.
This post discusses a local setup utilizing IBM Bob to generate an agent for watsonx Orchestrate, specifically with tools from the Galaxium Travels MCP server. It explains the architecture, customization of Bob, and integration with various components, providing both learning and practical implementation value for developers.
This blog post explores the paradox of AI's growth potential versus the increasing trend toward data protectionism. It highlights how AI tools are hindered by data access limitations, posing risks to innovation. The observation implies that as data becomes more valuable, organizations may withhold it, undermining the openness that has historically fueled AI development.
The article explores the potential for using the Model Context Protocol (MCP) as a primary backend interface instead of traditional REST APIs in AI-enabled applications. Through the Galaxium Travels experiment, it examines the advantages and disadvantages of an MCP-first architecture, advocating for its use to reduce duplication and complexity while acknowledging REST's established role in many ecosystems.
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.
The Model Context Protocol (MCP) is being increasingly utilized in AI applications, particularly with the watsonx Orchestrate ADK. This setup allows users to develop and manage agents and tools through a seamless integration of the MCP server and the Development Edition, enhancing user interaction and functionality in coding environments.
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.
Thomas Suedbroecker's Blog 