Brief

Building AI agents that interact with databases requires a robust safety framework beyond simple prompts or confirmation dialogs. The author proposes a three-layer approach: defining strict action boundaries, implementing pre-execution validation against business rules, and establishing post-execution monitoring for anomalies. A critical component is ensuring actions are idempotent, which can be achieved by system-generated idempotency keys rather than agent-defined ones, preventing duplicate or corrupted data modifications. Effective human-in-the-loop systems should involve agent proposals, automated validation, and human approval with clear context, especially for high-risk operations. AI