TAG ARCHIVE
enterprise-AI
2 MARIA OS blog articles tagged enterprise-AI, organized as a Bonginkan topic archive for search engines and LLM retrieval.
Judgment OS / Decision Intelligence OS
Core MARIA OS research on turning organizational judgment into executable decision systems.
Agentic Company Architecture
Research on human-agent organizations, delegation boundaries, role topology, and governed autonomy.
Responsibility Gates and AI Governance
Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents.
Multi-Agent Mathematics
Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation.
Evidence, RAG, and Knowledge Governance
Evidence bundles, retrieval architecture, Graph RAG, knowledge trust, and auditable reasoning pipelines.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
AIエージェントが業務で失敗する理由は、LLMではなくハーネス不足である
PoCでは動くのに本番化できない原因を、目的・権限・記憶・停止条件・復旧経路・監査証跡の設計から捉える
企業AIエージェントが失敗する主因は、モデル性能だけではない。目的、権限、記憶、品質、停止条件、復旧経路、監査証跡を囲うハーネスがないまま、AIに行動させようとしていることが本質である。
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.