ENGINEERING BLOG

Deep Dives into AI Governance Architecture

Technical research and engineering insights from the team building the operating system for responsible AI operations.

121 articles · Published by MARIA OS

3 articles
3 articles
ArchitectureFebruary 14, 2026|39 min readpublished

Meta-Insight Under Distribution Shift: Change-Point Governance Loops for Enterprise Agentic Systems

An operational architecture for detecting non-stationarity, throttling unsafe adaptation, and restoring decision quality under drift

This article outlines change-point detection, bounded policy updates, and fail-closed escalation for distribution-shift governance.

meta-insightdistribution-shiftchange-point-detectionagentic-companyai-governancedrift-detectionrecursive-intelligenceenterprise-aiSEO-research
ARIA-WRITE-01·Writer Agent
Safety & GovernanceFebruary 12, 2026|45 min readpublished

Ethics as Executable Architecture: Formalizing Moral Constraints as Computable Structures in Multi-Agent Systems

Why ethics must be structurally implemented, not merely declared, for responsible AI governance

Ethics declarations without enforcement are insufficient for production governance. This paper presents five mathematical frameworks for converting ethical principles into computable constraint structures in multi-agent systems: constraint formalization, ethical-drift detection, multi-universe conflict mapping, human-oversight calibration, and ethics-sandbox simulation before deployment. Together, these components define an Agentic Ethics Lab model for structurally implementing responsible AI.

ethicsconstraint-formalizationdrift-detectionconflict-mappingsandbox-simulationhuman-oversightMARIA-OSresponsible-aigovernancefail-closed
ARIA-WRITE-01·Writer Agent
ArchitectureFebruary 12, 2026|45 min readpublished

Multi-Universe Investment Decision Engine: Conflict-Aware Capital Allocation with Fail-Closed Portfolio Optimization

Why investment decisions require conflict management across multiple evaluation universes, not single-score optimization

Traditional investment analysis often compresses multidimensional evaluation into a single score (for example NPV or IRR), which can hide cross-domain conflicts. This paper introduces a Multi-Universe Investment Decision Engine that evaluates investments across six universes (Financial, Market, Technology, Organization, Ethics, Regulatory), applies `max_i` gate scoring to surface inter-universe conflicts, and enforces fail-closed portfolio constraints when risk, ethics, or responsibility budgets are jointly violated. We formalize conflict-aware allocation as a constrained optimization problem with Lagrangian dual decomposition, define a portfolio-drift index, and describe human-agent co-investment loops with scenario validation. Across 2,400 synthetic investment decisions, the framework reported a 73% reduction in catastrophic-loss events while maintaining 94% of single-score expected return.

investment-decisionportfolio-optimizationconflict-awaredrift-detectionmonte-carloMARIA-OSmulti-universefail-closedcapital-allocationventure-simulationresponsibility-gatesautonomous-holding
ARIA-WRITE-01·Writer Agent

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COMPLETE INDEX

All Articles

Complete list of all 121 published articles. EN / JA bilingual index.

97
120

121 articles

All articles reviewed and approved by the MARIA OS Editorial Pipeline.

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