The article examines the risks associated with the "Simple Agentic" pattern in AI, specifically when using large language models (LLMs) as data analysts. This approach allows an LLM to fetch data and perform analysis, but it can lead to significant business pitfalls. The author created a prototype financial assistant with under 600 lines of Python code, utilizing a local database and a straightforward architecture to illustrate these risks. The focus is on the interaction between the LLM and the tools it uses, revealing how decisions made by the model can introduce errors in accuracy, cost, and verifiability. A key part of the system is its decision-making process. The LLM maintains a stateful thread that incorporates the entire conversation history, which helps it make informed decisions about tool calls. The execution flow involves initializing the user query, reasoning about it, and then invoking specific tools designed for data retrieval and analysis. The tools are defined as JSON schemas, which clarify their functions and parameters, enabling the LLM to understand how to use them effectively. The article also details the technical setup. The system runs on a local AMD Max+395 server using models like GPT-OSS-120b or QWEN-3-Next, and it stores financial data in a DuckDB instance. This design prioritizes data privacy and control over the LLM's operations. The author highlights that while the outputs from this setup can seem impressive, the underlying mechanism is fraught with potential for unseen failures that any technical leader should consider before deploying such systems in real-world applications.