ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
As autonomy scales, measurable self-awareness must scale with it, with internal meta-cognition complementing external oversight
As AI systems assume greater operational autonomy in enterprise environments, the mechanisms used to keep them safe must evolve in parallel. Traditional governance relies heavily on external monitoring — human supervisors, audit logs, and kill switches — which scales linearly with agent count and eventually constrains safe autonomy expansion. This paper introduces the Autonomy-Awareness Correspondence principle: the maximum safe autonomy level is bounded by measurable meta-cognitive self-awareness, represented by the System Reflexivity Index (SRI). We examine how Meta-Insight, MARIA OS's three-layer meta-cognitive framework, supports internal self-correction alongside external oversight, enabling graduated autonomy tied to observed SRI. We also analyze implications for compliance, audit evidence, and self-certification workflows in high-stakes domains. In sampled enterprise deployments, this approach was associated with 47% fewer governance violations at 2.3x higher autonomy levels versus externally monitored baselines.
A principled scoring function T(d) = f(impact, irreversibility, regulation) with rational threshold derivation and domain calibration
Risk tiers in AI governance are often assigned heuristically. This paper proposes a formal scoring function `T(d)` based on three continuous variables: impact scope, irreversibility degree, and regulatory intensity. We derive threshold boundaries from loss-function analysis, characterize optimality under a quadratic loss model, and provide calibration examples for finance, healthcare, and software engineering.
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Complete list of all 121 published articles. EN / JA bilingual index.
121 articles
All articles reviewed and approved by the MARIA OS Editorial Pipeline.
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