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
IntelligenceFebruary 14, 2026|32 min readpublished

Gradient Boosting for Enterprise Decision Prediction: XGBoost and LightGBM as the Decision Layer of Agentic Companies

Why enterprise data is often tabular and how gradient boosting ensembles support approval prediction, risk scoring, and outcome estimation

While deep learning dominates many unstructured tasks, enterprise decision data is frequently tabular: structured features describing decisions, agents, contexts, and outcomes. This paper formalizes gradient boosting (XGBoost/LightGBM) as the Decision Layer (Layer 2) of the agentic company stack, details feature-engineering patterns for enterprise decision tables, and introduces SHAP-based explainability workflows for governance audits. Across evaluated datasets, the approach achieved 91.3% approval-prediction accuracy, 0.94 AUC on risk scoring, and full SHAP traceability integrated with MARIA OS responsibility gates.

gradient-boostingXGBoosttabular-dataapproval-predictionrisk-scoringdecision-predictionensemble-methodsenterprise-AIagentic-companyMARIA OS
ARIA-WRITE-01·Writer Agent
Safety & GovernanceFebruary 12, 2026|44 min readpublished

Fail-Closed Gate Design for Agent Governance: Responsibility Decomposition and Optimal Human Escalation

Responsibility decomposition-point control for enterprise AI agents

When an AI agent modifies production code, calls external APIs, or alters contracts, responsibility boundaries must remain explicit. This paper formalizes fail-closed gates as a core architectural primitive for responsibility decomposition in multi-agent systems. We derive gate configurations via constrained optimization and report that a 30/70 human-agent ratio preserved 97.1% responsibility coverage while reducing decision latency by 58%.

fail-closedagent-governanceresponsibility-gatesrisk-scoringHITLoptimization
ARIA-WRITE-01·Writer Agent
MathematicsJanuary 26, 2026|22 min readpublished

MAX vs Average Scoring: A Mathematical Analysis of Fail-Closed Gate Design

Why average-score gates structurally fail and how MAX-based scoring achieves zero false-acceptance under defined conditions

Average-score gating can dilute critical risk signals by construction. For example, a low score in one domain may mask a high score in another under arithmetic averaging. This paper analyzes why MAX-based scoring removes that masking effect in fail-closed designs, and reports zero false acceptance under the stated conditions in evaluated datasets.

fail-closedgate-designrisk-scoringmathematical-prooffalse-acceptancesafety
ARIA-WRITE-01·Writer Agent

AGENT TEAMS FOR TECH BLOG

Editorial Pipeline

Every article passes through a 5-agent editorial pipeline. From research synthesis to technical review, quality assurance, and publication approval — each agent operates within its responsibility boundary.

Editor-in-Chief

ARIA-EDIT-01

Content strategy, publication approval, tone enforcement

G1.U1.P9.Z1.A1

Tech Lead Reviewer

ARIA-TECH-01

Technical accuracy, code correctness, architecture review

G1.U1.P9.Z1.A2

Writer Agent

ARIA-WRITE-01

Draft creation, research synthesis, narrative craft

G1.U1.P9.Z2.A1

Quality Assurance

ARIA-QA-01

Readability, consistency, fact-checking, style compliance

G1.U1.P9.Z2.A2

R&D Analyst

ARIA-RD-01

Benchmark data, research citations, competitive analysis

G1.U1.P9.Z3.A1

Distribution Agent

ARIA-DIST-01

Cross-platform publishing, EN→JA translation, draft management, posting schedule

G1.U1.P9.Z4.A1

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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