Technical report demonstrates a control-plane governance runtime for autonomous AI, highlighting critical implementation gaps despite passing unit tests.
AI systems increasingly select and execute actions in non-stationary, partially observable, and high-consequence environments. This technical report presents Strategic Governance Runtime (SRG), a model-agnostic control-plane architecture that mediates proposed actions before execution through policy vetoes, time-aware state, adaptive thresholds, operating modes, outcome attribution, scoped authority and delegation, and decision artifacts. An artifact audit of the public TypeScript prototype at commit 71ff12c2f38baec40adbe5cb8aede2b78aa84392 found that all 194 unit tests pass, while the audited snapshot does not type-check or build and lacks reproducible benchmark evidence. The report therefore characterizes SRG as a research prototype, specifies required assurance invariants, and defines a reproducible validation agenda. This manuscript has not been peer reviewed.
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Mohammed Mazyad Alkhaldi (2026) studied this question.
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