What does it mean that agent reliability metrics?

Track how often each agent fires when it should and does not fire when it should not.

Example: Consider your morning exercise agent — the cognitive routine that gets you out of bed and into a workout. Over the past thirty days, there were twenty-two weekday mornings where the trigger condition was met (alarm at 6:00 AM, no illness, no travel). The agent fired on eighteen of those mornings. It failed to fire on four. It also fired on two weekend mornings when you had explicitly designated rest days — the agent activated when it should not have. Your reliability rate is 18/22 = 81.8%. Your false-fire rate is 2/8 = 25%. These two numbers together tell you something that 'I usually exercise' never could: your agent is moderately reliable but has a specificity problem. It does not discriminate well between trigger and non-trigger conditions. That specificity problem is where your optimization effort should go — not toward more motivation, but toward sharper trigger discrimination.

Try this: Select three cognitive agents you rely on regularly — your daily planning agent, your emotional regulation agent during conflict, your focused-work agent, your active-listening agent, or any others you have identified in earlier phases. For each agent, define: (1) The trigger condition — what situation should activate it? (2) The observation window — the past 14 days. (3) The hit count — how many times the trigger occurred and the agent fired correctly. (4) The miss count — how many times the trigger occurred and the agent failed to fire. (5) The false-fire count — how many times the agent fired when the trigger condition was not actually met. Calculate reliability rate (hits / total triggers) and false-fire rate (false fires / non-trigger occasions) for each. You now have six numbers that describe the reliability profile of three core agents. Write them down. This is the beginning of your monitoring dashboard's reliability layer.