Artificial intelligence systems are difficult to reproduce because their behavior depends on nondeterministic models and on data, configurations, policies, and external services that continue to change. But exact reproducibility is not always what operators, investigators, or auditors need. They often need something different: historical reconstructability. The central claim is that reconstructability is a system property established during execution, not inferred later from surviving artifacts. To make that property concrete, this article introduces Orrery, a reference architecture for preserving the runtime bindings and historical dependency states needed to reconstruct an AI system’s past execution.

Imagine an AI-assisted decision that must be examined six months after it was made. Rerunning the system may produce a different answer, but an investigator may still need to determine which model endpoint, policy, retrieved data, tool contract, and runtime configuration were used at the time.…