Drift reopens the evolutionary loop
A locally converged ecology can become unfit when the workload changes. Drift is the signal that the no-op state may no longer be optimal.
Drift signals
| Signal | Meaning |
|---|---|
| Input distribution change | users are asking different questions |
| Error slice increase | a specific task family is degrading |
| Confidence mismatch | calibration no longer matches outcomes |
| Cost shift | latency, energy, or memory changed under load |
| Escalation spike | specialists no longer cover the task mix |
| Policy change | the acceptable action space changed |
Trigger logic
FUNCTION drift_trigger(window, baseline, policy)
IF DISTANCE(window.input_distribution, baseline.input_distribution) > policy.input_shift_limit
RETURN OPEN_BREEDING_CYCLE("Input shift")
END IF
IF window.error_rate_by_slice.MAX_DELTA > policy.error_shift_limit
RETURN OPEN_BREEDING_CYCLE("Error slice degradation")
END IF
IF window.calibration_error > policy.calibration_limit
RETURN OPEN_BREEDING_CYCLE("Calibration drift")
END IF
RETURN NO_OP("No material drift")
END FUNCTIONResponding to drift
Do not immediately fine-tune the champion. First classify the drift. Some drift is better handled by routing, retrieval, documentation, threshold changes, new specialists, or retirement of misleading modules.
Drift versus noise
Require persistence before structural changes. A single bad day may be noise. A repeated slice degradation across multiple windows is stronger evidence.
Frozen reference
Keep a frozen reference benchmark so the system can distinguish genuine environmental shift from regression introduced by the current population.
Source reports used for this guide
These reports are preserved verbatim in the site archive. The guide above is an editorial synthesis and may narrow, qualify, or reorganize claims from the source material.