Five design rules that keep AI agents running reliably despite constant interruptions

Building long-running AI agents requires solving a core challenge: preserving meaningful state across frequent interruptions, reboots, or scheduled restarts that wipe working memory. The author recommends maintaining a single source-of-truth file, updated after every step, written as if the next reader has total amnesia. Before taking any action, the agent should re-verify real-world conditions rather than rely on remembered assumptions, since stale beliefs can corrupt ongoing work. Every action must also be designed to be safely repeatable, so an interruption mid-task results in a harmless retry rather than a broken state. Finally, work should be chunked into units small enough to complete within a single run, with outcomes persisted at each boundary so that being killed between steps carries no cost.