A Deterministic Architecture for Pre-Execution Governance

Artificial intelligence systems require reliable pre-execution mechanisms to ensure contextual
integrity, traceability, and internal coherence before actions are carried out. Terms such as
clarity and awareness are frequently used in governance discourse, yet they are rarely
formalized in operational terms.
This paper introduces PREEXEC™, a deterministic pre-execution architecture that quantifies
Clarity as a structural coherence state and Awareness as a composite of State, Intent, and
Context Awareness. Normalized sub-scores, deterministic thresholds, and a versioned audit
trail (AuditChain) enable reproducible release decisions, traceability, and systems-level
monitoring.
The architecture builds on established concepts from context-aware computing, intention
modeling, systems-theoretic safety, and AI risk management, providing a practical foundation
for higher-order governance layers.