What does it mean that reliability optimization?

A reliable agent works every time, not just when conditions are perfect.

Example: You built an agent that triggers a weekly review every Sunday morning. It works when you're home, rested, and have nothing planned. But on travel weekends, holidays, or after a bad night's sleep, the agent fails silently — you skip it without noticing. You don't have a reliability problem with the review itself. You have a reliability problem with the trigger-to-execution chain: the agent only fires under ideal conditions. Reliability optimization means redesigning the agent so it fires in degraded conditions too — a shorter version for travel, a voice-memo fallback for low energy, a Tuesday recovery protocol when Sunday breaks.

Try this: Pick one agent (habit, routine, automated behavior) that you consider important but that fails more than 20% of the time. Map every instance in the last 30 days where it fired and where it didn't. For the failures, identify the specific condition that broke it — fatigue, travel, interruption, ambiguity, emotional state. Now design one structural change that addresses the most common failure mode. Implement it for two weeks and track the new firing rate.