Principles
2,201 principles · page 4 of 46
Write monitoring entries immediately after agent execution…
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…
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…
Include at least one qualitative assessment dimension in tracking to resist gaming the metric through checkbox completion.
Treat monitoring data as experimental evidence requiring…
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…
Set thresholds at approximately three standard deviations from measured baseline to balance detection sensitivity against false alarm rate.
Define thresholds before observing problems rather…
Define thresholds before observing problems rather than retroactively to preserve threshold as commitment device rather than rationalization.
Implement tiered alert thresholds (warning/action)…
Implement tiered alert thresholds (warning/action) to provide lead time on degrading performance before catastrophic failure.
Calibrate threshold tightness to the cost ratio of false…
Calibrate threshold tightness to the cost ratio of false alarms versus misses rather than perfectionism or comfort.
Maintain signal-to-noise ratio above approximately 80% (at…
Maintain signal-to-noise ratio above approximately 80% (at least four true positives per false positive) to prevent alert fatigue and habituation.
Monitor trends across time windows rather than point-in-time
Monitor trends across time windows rather than point-in-time snapshots to detect gradual degradation before threshold failures occur.
Build system trust through repeated experience…
Build system trust through repeated experience of the complete loop (capture → process → retrieve → act) rather than through system sophistication, as trust is psychological not architectural.
Calculate moving averages over fixed observation windows…
Calculate moving averages over fixed observation windows to filter noise and reveal underlying signal direction in performance metrics.
Compare performance across adjacent time windows to detect…
Compare performance across adjacent time windows to detect directional change without requiring visualization infrastructure.
Track rate of change in metrics to determine urgency…
Track rate of change in metrics to determine urgency of intervention, not just direction of change.
Distinguish seasonal patterns from genuine trends to avoid…
Distinguish seasonal patterns from genuine trends to avoid intervening in normal cyclical variation.
Store dated observations rather than relying on memory…
Store dated observations rather than relying on memory to enable retrospective trend analysis.
Reduce monitoring signals to fewer than can saturate…
Reduce monitoring signals to fewer than can saturate attention, even when this means discarding potentially useful metrics.
Review monitoring system design quarterly to audit whether…
Review monitoring system design quarterly to audit whether tracked metrics still correlate with outcomes that actually matter.
When a signal is missed, reduce total monitoring volume…
When a signal is missed, reduce total monitoring volume rather than adding new alerts to an already-saturated system.
Establish baseline periods before implementing changes…
Establish baseline periods before implementing changes to distinguish intervention effects from temporal variation.
Document comparative advantage profiles showing which agent…
Document comparative advantage profiles showing which agent excels on which dimensions rather than declaring absolute winners.
Remove high-salience stimuli from your environment before…
Remove high-salience stimuli from your environment before beginning focused work to reduce involuntary attentional capture and the cognitive cost of suppression.
Replace vanity metrics that cannot guide action…
Replace vanity metrics that cannot guide action with actionable metrics that have clear causal links to controllable variables.
Log configuration changes and outcomes to build experiment…
Log configuration changes and outcomes to build experiment history that transforms optimization from guesswork to pattern recognition.
Maintain qualitative observation alongside quantitative…
Maintain qualitative observation alongside quantitative metrics to detect when metric improvement correlates with outcome degradation.
Direct optimization effort exclusively at the system's…
Direct optimization effort exclusively at the system's current constraint, because improvements to non-constraints produce local gains that do not propagate to system-level output.
Before investing resources to expand constraint capacity…
Before investing resources to expand constraint capacity, extract maximum throughput from the constraint as it currently exists by eliminating waste, idle time, and unnecessary overhead at the bottleneck.
Configure all non-constraint processes to optimize…
Configure all non-constraint processes to optimize the constraint's throughput rather than their own local efficiency, because subordinating non-constraints to constraints increases system output while optimizing non-constraints does not.
After successfully improving a constraint, immediately…
After successfully improving a constraint, immediately re-identify the system's new bottleneck, because constraint improvement shifts the limiting factor to a different component.
Measure actual time consumed by each system component before
Measure actual time consumed by each system component before optimizing, because subjective experience of difficulty does not correlate reliably with objective constraint status.
Direct each optimization iteration at the single variable…
Direct each optimization iteration at the single variable identified as the current bottleneck, then measure results before changing additional variables, because simultaneous multi-variable changes prevent causal attribution.
Define success criteria and measurement methods before…
Define success criteria and measurement methods before implementing any optimization change, because post-hoc evaluation enables unconscious redefinition of success to match whatever occurred.
Schedule cognitively demanding work during your measured…
Schedule cognitively demanding work during your measured peak attention window and batch shallow work outside it to maximize output quality per unit of effort invested.
When monitoring data shows stable satisfactory performance…
When monitoring data shows stable satisfactory performance with no identifiable bottleneck, redirect optimization effort to a different system rather than continuing to optimize the current one.
Implement each optimization change on a small, contained…
Implement each optimization change on a small, contained scale before system-wide deployment, because localized testing isolates effects and limits damage from failed experiments.
Periodically examine and revise the foundational assumptions
Periodically examine and revise the foundational assumptions underlying your optimization efforts, not just the tactical adjustments within those assumptions, because paradigmatic constraints often limit throughput more than process constraints.
Measure where the system actually breaks down before…
Measure where the system actually breaks down before allocating optimization effort—optimizing components that account for a small fraction of total constraint yields negligible improvement regardless of optimization quality.
Establish explicit stopping criteria before beginning any…
Establish explicit stopping criteria before beginning any optimization effort, specifying the threshold that defines 'good enough' and the action that occurs when that threshold is met.
As time remaining until deployment or deadline decreases…
As time remaining until deployment or deadline decreases, shift optimization effort from exploring alternative approaches to exploiting the best known approach, because exploration costs cannot be recouped over short time horizons.
Allocate optimization effort proportionally…
Allocate optimization effort proportionally to the improvement each component can still yield rather than proportionally to each component's current performance level, because low-performing components may already be near their ceiling while high-performing components may have headroom.
Pre-register evaluation criteria before running experiments…
Pre-register evaluation criteria before running experiments to prevent post-hoc rationalization from contaminating result interpretation.
Change only one variable at a time when testing improvements
Change only one variable at a time when testing improvements so that observed effects can be attributed to specific causes rather than confounded with simultaneous changes.
When multiple changes are made simultaneously…
When multiple changes are made simultaneously, systematically remove each component individually to determine its contribution, rather than assuming all components contribute equally or that the most salient component is most important.
Batch information consumption into scheduled windows rather…
Batch information consumption into scheduled windows rather than processing it continuously, because task-switching destroys flow states and imposes recovery costs far exceeding the interruption duration.
Record each isolated change and its measured effect to build
Record each isolated change and its measured effect to build a searchable optimization history that enables bisection debugging when performance degrades.
Eliminate unnecessary process steps entirely before…
Eliminate unnecessary process steps entirely before attempting to make necessary steps faster, as structural removal produces larger gains than incremental speed improvements.
Reduce agent execution time to lower the motivation…
Reduce agent execution time to lower the motivation threshold required for initiation, as faster behaviors fire more frequently at lower motivation levels than slower ones producing identical outputs.
Measure agent execution frequency after speed optimization…
Measure agent execution frequency after speed optimization, not just duration, as the primary value of faster agents is increased firing rate rather than time savings per execution.