Deterministic Execution

After this lesson, the reader should understand:

• 01State the conditions required for deterministic evaluation.
• 02Identify sources of non-determinism in a governance pipeline.
• 03Distinguish a deterministic artifact from a probabilistic model.

The model that changed its mind

A language model applied the same policy to the same case twice and produced different outcomes. Where is the failure?

Not in the model — models are probabilistic by construction. The failure is architectural: the authority was carried inside the model instead of inside an artifact the model calls. When the artifact evaluates and the model only assists with fact extraction and presentation, the outcome becomes reproducible while the model remains useful for the parts that do not require determinism.

• Where should a model sit relative to the authority layer?
• Is determinism achievable when facts arrive from external systems?
• What tolerance, if any, is acceptable in a governance outcome?

Audit one automated decision path for non-determinism.

1. 01List every input, including implicit ones such as time.
2. 02Mark any input that can change between runs.
3. 03Mark any step whose output is not a function of its inputs.
4. 04Propose how to fix or record each source of variance.

• Reproducible builds and computation.
• Determinism in distributed systems.
• Hybrid neuro-symbolic architectures.