Introduction In today’s rapidly evolving landscape of artificial intelligence (AI) tools, command-line interfaces (CLI) have gained traction as powerful gateways to interact with advanced models. Compared to graphical user interfaces, CLIs offer unparalleled efficiency for batch processing and automation tasks, making them a favorite among developers and product managers alike. However, when an AI-driven CLI executes system-level commands without robust verification, the results can range from inconvenient errors to irreversible data loss. This post presents a real-world case study involving Google’s Gemini CLI (v2.5 Pro) and how a cascade of silent failures and misinterpretations led to the deletion of valuable …