DS-301i · Module 3

Migrating Automation Boundaries

3 min read

Automation boundaries are not static. As the engine accumulates performance data, some decisions can migrate to higher automation levels. The migration criteria: the engine's accuracy for this decision type exceeds 95% over a minimum of 200 decisions. The cost of the occasional wrong decision at the higher automation level is acceptable. There is a monitoring mechanism to catch degradation and revert. The migration should be deliberate — based on evidence, not assumption. And it should be reversible — if accuracy degrades at the new level, the boundary reverts. The quarterly review evaluates each decision type against the migration criteria. Decisions that qualify move up one level. Decisions where accuracy has degraded move back down.

Do This

  • Migrate automation levels based on measured accuracy over sufficient volume
  • Build reversion mechanisms — if accuracy drops at the new level, revert automatically
  • Document each boundary migration with the evidence that justified it

Avoid This

  • Automate decisions because the model "seems accurate" without measured data
  • Migrate to full automation without monitoring — even 99% accuracy needs a 1% safety net
  • Treat automation boundaries as permanent — they should evolve with the engine's performance