Deterministic agent governance evaluates explicit, machine-checkable constraints using ordinary code and policy checks. It does not ask a second model to decide whether the first model complied, so the same inputs produce an inspectable result.
The attraction of an LLM judge
A model can read natural-language policy and a patch, then produce a nuanced compliance opinion with little setup. That is useful for review assistance and exploration. It is a weaker foundation for a hard merge claim because the output can vary with model, prompt, context and sampling behaviour.
What deterministic governance can prove
Deterministic checks work well for structural boundaries, forbidden dependencies, required files, schema properties, API surfaces and other conditions with unambiguous evidence. They are testable, cacheable and debuggable using the same engineering practices as the rest of CI.
The cost is narrower coverage. A deterministic system should expose that limit instead of expanding claims through vague interpretation.
Keep model review in the right role
A model can still suggest which decisions a patch may affect, explain a failure or flag a judgement-based concern for a human. Those outputs should be labelled advisory. The blocking result should come from an explicit check when the team describes it as enforced.
A truthful governance report
Report enforced, advisory and unclassified decisions separately. Include scope and evidence for every deterministic result. This gives teams a smaller but defensible guarantee and a visible backlog for decisions that have not yet been classified.