The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types and 2 molecules
Measure time-to-activation as a core performance metric — speed of response often determines whether a correct response is useful.
Use percentile distributions rather than averages to measure agent performance under varying conditions — P50, P95, and P99 expose different failure modes.
Reduce the number of alternatives an agent must evaluate to decrease response latency — discrimination time scales logarithmically with choice count.
Implement if-then trigger-response structures to achieve strategic automaticity — explicit trigger specification creates faster activation than goal-alone approaches.
Add debouncing (requiring conditions to persist for a duration) to reduce false positive rates without tightening triggers — time filters noise from signal.
Schedule explicit negative audits asking 'what should have triggered but didn't' rather than waiting for agents to fire — false negatives don't announce themselves.
Make capture tools visible and accessible at eye-level or top-of-stack in every context, because visibility determines attention and proximity determines behavior independent of conscious intention.
Externalize agent specifications in written form as the reference for drift detection — comparison requires a fixed baseline outside memory.
Implement scheduled automated comparisons between agent specification and actual behavior — humans do not spontaneously notice gradual degradation.
Distinguish activity monitoring from purpose monitoring — an agent can fire reliably while producing output that no longer serves its original goal.
Rotate measurement targets periodically to prevent optimization for the metric displacing optimization for the outcome.
Define 'decision enabled' as the success criterion for each monitoring activity—if no decision changed in the past 30 days, eliminate the monitoring.
Separate monitoring into quantifiable metrics (automate), pattern recognition (semi-automate), and meaning-making (manual reflection).
Establish dynamic baselines from actual performance data rather than setting thresholds from aspirational targets.
Schedule periodic meta-monitoring reviews to verify that automated monitoring systems remain calibrated to changing reality.
Review accumulated journal entries analytically rather than chronologically to detect patterns invisible in single observations.
Include both successes and failures in monitoring logs to prevent perception bias toward system dysfunction.
Increase ability and improve prompts rather than relying on motivation to sustain capture behavior, as motivation fluctuates unpredictably while environmental triggers remain stable.
Write monitoring entries immediately after agent execution rather than batching at day's end to capture accurate context and reduce memory reconstruction errors.
Make tracking data physically visible in your daily environment rather than requiring deliberate access to close the feedback loop.
Include at least one qualitative assessment dimension in tracking to resist gaming the metric through checkbox completion.
Treat monitoring data as experimental evidence requiring curiosity rather than verdict requiring judgment to prevent shame-based abandonment.
Set thresholds at approximately three standard deviations from measured baseline to balance detection sensitivity against false alarm rate.
Define thresholds before observing problems rather than retroactively to preserve threshold as commitment device rather than rationalization.