Modern Artificial Intelligence systems — ranging from large language models to multi-agent architectures and vision classifiers — are fundamentally nondeterministic, opaque, and prone to unpredictable failure modes. These characteristics make them structurally incompatible with safety-critical environments such as robotics, autonomous vehicles, industrial automation, and real-time operational control loops. In this paper, I introduce Lume-V, a deterministic governance layer that validates, explains, certifies, and arbitrates AI decisions before they reach downstream systems. Rather than attempting to force determinism onto the probabilistic models themselves, Lume-V acts as an immutable state machine wrapper. It enforces a strict set of seven non-negotiable safety invariants, generates deterministic explainability traces, issues Ed25519-signed trust certificates anchored to the Lume Trust Certificate (LTC v1.0) architecture, and performs multi-agent consensus-by-safety arbitration. I define a 10-layer architectural specification for Lume-V and demonstrate its effectiveness through a drone control simulation where injected timing and logic faults are safely intercepted, yielding 24 validated decisions, 18 approvals, 6 deterministic overrides, and zero unsafe actuator commands at an average latency of 4ms. Lume-V establishes the foundational mechanism for Deterministic Autonomous Infrastructure Governance Systems (DAIGS), providing a practical, rigorous, and verifiable bridge between nondeterministic AI and real-world systems requiring deterministic safety guarantees.
Ronald Jason Andrews (Thu,) studied this question.