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.

Effective evaluation of agent performance requires a combination of end-to-end evaluations and "N - 1" simulations to identify issues and improve functionality. While external tools can assist, it's critical to develop tailored evaluations based on specific use cases and to continuously monitor agent interactions for optimal results. Checkpoints within prompts can help ensure adherence to desired conversation patterns.