Exploratory computational systems—such as scientific simulations, AI research pipelines, and autonomous experimentation frameworks—are especially vulnerable to intermittent execution failures that do not reliably surface as hard errors. These failures often arise from silent parameter mismatches, interface drift, nondeterministic execution semantics, missing artifacts, or late-stage logging crashes, and can corrupt results while remaining difficult to detect. This paper introduces a fail-closed execution governance architecture designed to eliminate such nondeterministic failure modes without constraining exploratory flexibility or development velocity. The proposed approach enforces correctness dynamically at execution time through mandatory preflight validation, runtime interface enforcement, deterministic seed authority, and artifact-first execution control. Rather than relying solely on static typing, unit tests, or continuous integration pipelines, the framework treats execution validity as a first-class systems concern. Every execution attempt either produces authoritative, auditable artifacts or fails early with explicit classification, preventing “successful but incorrect” runs from contaminating results. Empirical observations from long-running exploratory workflows demonstrate that the architecture eliminates persistent intermittent failures while preserving rapid iteration. The framework is model-agnostic, language-independent, and applicable across domains including AI research, numerical simulation, autonomous systems, and distributed computational workflows. This work reframes execution correctness as an infrastructure-level governance problem and provides a practical foundation for reliable, interpretable, and reproducible computation in evolving research environments.