how do you keep long-running AI agents from quietly going off the rails?

A developer community discusses strategies to prevent long-running AI agents from drifting or inventing unrequested plans, as crashes are rare. The conversation explores practical solutions beyond just "better prompts," such as hard checkpoints, evaluation loops, smaller scoped tools, or human intervention at regular intervals, seeking real-world practices to maintain agent alignment and efficiency.

This discussion moves beyond theoretical solutions to focus on practical, real-world strategies like hard checkpoints or human gating, unlike typical advice centered on prompt engineering.