This article explores the risks associated with the "Simple Agentic" pattern in AI systems, where a language model analyzes data fetched from external tools. The author details a prototype financial assistant, highlighting how this approach can lead to hidden failures in accuracy and verifiability.

The article discusses the potential security risks associated with using large language models (LLMs) in coding practices. It highlights how these models can inadvertently introduce vulnerabilities and the implications for developers and organizations. The need for robust security measures when integrating LLMs into coding workflows is emphasized.