I want to share my experience in the cloud, AI, and agents development areas.
This post shows the importance of clear prompt structure when developing local AI agents with frameworks like LangGraph and Ollama. Smaller models are less tolerant of ambiguities, making it crucial to separate instructions, context, and output formats. This enhances reliability, debugging, and reduces risks from untrusted inputs.
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 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.
This blog post details the development and deployment of a customizable AI Agent using watsonx.ai. It covers motivations, architecture, and code for a weather query tool, explaining local execution, testing with pytest, and deployment via scripts. The integration with Streamlit UI is emphasized, showcasing seamless deployment processes and enhanced functionality for developers.
Thomas Suedbroecker's Blog 