Designing AI Agents to Limit Damage When They Inevitably Fail

A software engineering guide argues that AI agent failures are unavoidable, making the real design challenge about limiting how much harm a failure can cause. The core principle is granting agents only the minimum authority needed for a task, so that an incorrect action has the smallest possible consequence. High-stakes or irreversible actions — such as sending emails, issuing refunds, or moving money — should require human approval, while low-risk reversible actions can run autonomously. Developers are advised to build in dry-run modes, soft deletes, and thorough logging so that errors can be detected quickly and rolled back where possible. The approach frames AI reliability as a measurable metric, suggesting that an agent's permitted capabilities should scale with its demonstrated trustworthiness on a given task.