Lifecycle Drift

Purpose

This page documents how system behavior changes over time and how those changes are detected. This layer exists to surface degradation caused by data updates, shifting assumptions, evolving usage patterns, and gradual erosion of governing rules.

In practice, this is the difference between a system that quietly changes its behavior over time and one where those changes are surfaced before they undermine trust.

It defines how drift is identified before it produces misleading outputs that appear correct but no longer reflect system intent or authority.

Scope

Includes identification of drift signals, monitoring of assumption validity, and criteria for intervention, rollback, or shutdown. Excludes performance tuning, model retraining strategies, and any assumption that drift can be fully prevented or safely ignored.

Constraints

• Assumptions documented at system design time are periodically re-evaluated against current data and usage.
• Changes in source material, retrieval behavior, or usage patterns are treated as potential drift signals.
• Undetected or unaddressed drift is treated as a system failure condition, not a tolerable operating state.
• Corrective action prioritizes restoring documented constraints over expanding scope or relaxing boundaries.

These constraints acknowledge that AI systems degrade by default. Drift control exists to limit damage, not to promise stability.

Notes

Drift rarely presents as explicit error. Systems without explicit drift detection tend to fail silently while continuing to produce outputs that appear coherent and confident.