Use this when

Many cases in a workflow are clear-cut and follow a stable rule, while a minority genuinely need interpretation.

The pattern

CASE ARRIVES
      ↓
FIXED RULES handle the clear cases
      ↓
NOT COVERED BY A RULE?
      ↓
AI handles the genuinely ambiguous ones

The deterministic rules run first and settle everything they can. Only the cases the rules cannot resolve reach the model.

Why it works

Rules are cheap, fast, consistent and easy to test. When they can settle a case, they should, because a deterministic answer is more reliable than a probabilistic one. Reserving the AI for the cases that actually need interpretation gives you the best of both: consistency where the answer is clear, and interpretation where it is not.

It also reduces cost and risk. Fewer cases go to the model, so there is less to review and less that can go wrong. This is decomposition applied to routing the work itself.

How to apply it

  1. Identify the clear-cut cases that follow a stable rule.
  2. Handle those with deterministic automation.
  3. Pass only the remaining, genuinely ambiguous cases to the AI.
  4. Keep a person available for what the AI is unsure about.

Good fit

  • Workflows where most cases are simple and a few are hard.
  • Situations where consistency matters for the clear cases.

Poor fit

  • Workflows where almost every case needs interpretation, so rules cover little.
  • Cases where writing the rules is harder than letting the AI handle everything with review.

What this does not solve

Handling clear cases with rules improves reliability and cost; it does not make the AI's part of the work correct. The ambiguous cases still need good design, review and testing. The pattern narrows where AI risk lives; it does not remove it.