I Built an AI Agent That Could Call APIs. Then I Had to Teach It When NOT to Call Them.

Building an AI agent capable of executing API calls is straightforward, but ensuring it does so safely is a complex engineering challenge. The author details their experience creating a robust permission layer to prevent AI agents from performing unauthorized or dangerous actions. By decoupling tool selection from authorization, the system treats the model's output as a request rather than a command. This architecture incorporates a deterministic permission engine that validates tool names, arguments, and resource ownership before execution. The author emphasizes that relying solely on system prompts for security is insufficient due to the probabilistic nature of LLMs and risks like prompt injection. Instead, they advocate for explicit code-based validation, audit logging, and human-in-the-loop approval for sensitive operations. This approach treats AI agents as part of a secure distributed system, applying the principle of least privilege to ensure reliable and safe automation.