I want to share my experience in the cloud, AI, and agents development areas.
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 blog post demonstrates the usage of the web channel functionality in watsonx Orchestrate, enabling the embedding of conversational AI agents into custom web applications. It guides users through setting up a remote environment, generating source code, and running a web server to invoke chat features, emphasizing ease of use and customization options.
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 content provides a solution for resolving the 'DCO is missing' error encountered when forking a GitHub project. It outlines steps to amend commits with sign-off, including adding a commit-msg hook script. Successfully following these instructions helps ensure that your pull request functions correctly.
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.
This blog discusses the emergence of agentic AI, capable of planning and executing complex tasks autonomously, contrasting with traditional generative AI. The post emphasizes the importance of managing expectations, oversight, and ensuring transparency due to the unpredictability, including potential hallucinations associated with these systems. LangGraph is highlighted as a powerful tool for developing agentic workflows.
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.
The post explores the integration of Large Language Models (LLMs) in applications, stressing the need for effective production testing. It introduces the AI Operational Complexity Cube concept, emphasizing new testing dimensions for LLMs, including prompt testing and user engagement. A structured testing approach is proposed to ensure reliability and robustness.
Thomas Suedbroecker's Blog 