Audit Replay: The Forgotten Half of Reproducibility
Reproducibility isn't rerunning the model. It's reconstructing a specific past decision — as it was made, against the state that produced it — without invoking the model at all. Most enterprise AI systems can do the first. Almost none can do the second. And the second is the one regulators are asking about.
This is article 10 in a series on the engineering economics of regulated AI. Prior pieces have covered provenance graphs, multimodal validation for climate risk, runtime enforcement, the reproducibility crisis, attribution stability, deterministic orchestration, the cost model for auditable AI, and the escalation layer that produces the persisted state audit replay operates against. The next piece will address model version pinning as a regulatory primitive — why "we upgraded to a newer model" is the wrong answer to a compliance query, and what the right architectural pattern looks like.
Nine months into the live operation of the...
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