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.
It’s All About Risk-Taking: Why “Trustworthy” Beats “Deterministic” in the Era of Agentic AI
This post explores how Generative AI and Agentic AI emphasize trustworthiness over absolute determinism. As AI's role in enterprises evolves, organizations must focus on building reliable systems that operate under risk, balancing innovation with accountability. A personal perspective.
The Rise of Agentic AI and Managing Expectations
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.
