Deterministic AI Enforcement
Definition
An approach to AI governance where policy decisions are made through code-based logic gates rather than probabilistic model inference. In deterministic enforcement, the same input always produces the same output — there is no randomness, no temperature, and no probability distribution. The decision is computed, not inferred.
Why It Matters
LLM-based validation (using one model to check another) has inherent error rates — the validator itself can hallucinate. Deterministic enforcement eliminates this: if an action matches a blocked pattern, it is blocked. No probability. No error rate. No "usually works." This is the difference between software engineering and prompt engineering.
How Exogram Addresses This
Exogram's entire policy engine is deterministic Python logic — zero LLM inference in the decision path. 8 policy rules run as code gates: schema enforcement, boundary control, loop protection, destructive action blocking, data exfiltration prevention, and more. Same input → same output → every time. 0.07ms evaluation.
Related Terms
Key Takeaways
- → Deterministic = same input → same output → every time
- → LLM validation has an inherent, irreducible error rate
- → 0.07ms code gates vs 50-200ms LLM inference
- → This is the difference between software engineering and prompt engineering