TAG ARCHIVE
agent-governance
12 MARIA OS blog articles tagged agent-governance, 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.
How Enterprises Should Adopt MARIA OS: AI Implementation Talent, Responsibility, and Governed Autonomy
A practical operating model for introducing MARIA OS into enterprise workflows without turning AI into the decision-maker
Enterprise AI adoption fails when automation advances faster than responsibility design. This article explains how MARIA OS should be introduced through a three-layer model: automate L1 operations, support L2 judgment patterns, and keep L3 responsibility architecture human-owned.
エンタープライズにMARIA OSを導入する方法: AI実装人材、責任設計、統治された自律性
AIを意思決定者にせず、MARIA OSを企業業務へ導入するための実務的な三層モデル
エンタープライズAIは、自動化が責任設計を追い越した瞬間に止まる。本稿では、MARIA OSをL1操作の自律化、L2判断パターンの支援、L3責任アーキテクチャの人間継承という三層モデルで導入する方法を整理する。
Operational AI Governance as a Technical Moat: A Realistic Assessment of MARIA OS
Why internal auto-recovery, external HITL, responsibility envelopes, and fail-closed gates matter more than another agent demo
The next credible enterprise AI advantage will not come from claiming full autonomy. It will come from knowing where autonomy must stop, how recovery paths are tested, and how human accountability survives at production speed. This article gives a realistic assessment of Bonginkan's MARIA OS architecture and the operational evidence required to turn that architecture into a durable technical moat.
運用されるAIガバナンスは技術的優位性になるか：MARIA OSの現実的評価
内部では自動復旧を攻め、外部ではHITLを厚くする。責任契約・fail-closed・回復経路を実装レイヤーで見る
企業AIの次の優位性は、完全自律を主張することではなく、どこで止めるか、どう復旧するか、人間の責任をどう残すかを本番運用で証明することから生まれる。本稿では、ボンギンカンのMARIA OSが持ちうる技術的優位性と、グローバル・日本市場での現実的な位置づけを、過剰な断定を避けて評価する。
安全性はfan-inに宿る：fail-closedな並列マルチハーネス設計
エージェント基盤で複数のHarnessを並列実行しても安全性を弱めないための5つの実装規律
エージェント基盤では、1つのactionに対してidentity、authority、trust、surface固有のHarnessを同時に走らせたくなる。しかしfail-closedなsystemでは、素朴な並列化が安全性を静かに弱める。この記事では、正規化されたenvelope列に対するfan-in fold、timeoutの制限側変換、DAG依存、budget、snapshotの設計規律を実装レベルで整理する。
Autonomous Repair Harness: Turning Runtime Failures into Safe, Reviewable System Improvements
Failure episodes, repair proposals, rollback envelopes, and approval boundaries for self-healing agentic systems
Automatic repair is the next step after automatic implementation. A dynamic harness can observe runtime failures, classify drift, draft repairs, replay evidence, and route patches through rollback and approval boundaries without allowing agents to rewrite their own constitution.
自動改修ハーネス：Runtime Failureを安全でReview可能な改善へ変換する
Failure episode、repair proposal、rollback envelope、approval boundaryによるself-healing agentic system
自動改修は自動実装の次段階である。Dynamic harnessはruntime failureを観測し、driftを分類し、repairを下書きし、evidenceをreplayし、rollbackとapproval boundaryを通してpatchをrouteできる。ただしagentが自分自身の憲法を書き換えることは許さない。
Company Intelligence: Why MARIA OS Is Not an AI Tool but the Operating System for Organizational Judgment
From memory and decision cards to strategic simulation, this is the architecture that turns AI Office from labor automation into an organization that learns
Most AI deployments improve local productivity but fail to compound into institutional intelligence. This article defines Company Intelligence as the closed loop of memory, decision, feedback, and governance, then explains how MARIA OS encodes that loop into company memory, executable decisions, agent performance systems, reflection pipelines, knowledge graphs, and strategic simulation.
Company Intelligence: なぜMARIA OSはAIツールではなく、会社の知能をつくるOSなのか
AI Officeの価値は作業自動化ではなく、会社が記憶し、判断し、学習し、自己改善する閉ループを持てるかで決まる
多くのAI導入は局所的な生産性を改善しても、企業固有の知能には積み上がらない。本稿は、Company Intelligence を Memory・Decision・Feedback・Governance の閉ループとして定義し、MARIA OS がそれを Company Memory、Decision Card、Task Intelligence、Agent Performance、Knowledge Graph、Strategic Simulation へどう実装するかを解説する。
From AI Office to Agent HR OS: The Operating Stack for Human + AI Organizations
Why AI Office, AI Office Building, and Agent HR OS should be understood as one connected system for operating AI employees, not just using AI tools
Enterprise AI is moving from isolated assistants to managed AI labor. This article explains how AI Office provides the workplace layer, AI Office Building provides organizational topology, and Agent HR OS provides the HR and governance layer for recruiting, evaluating, promoting, and operating AI employees inside a Human + AI Organization.
AI OfficeからAgent HR OSへ: Human + AI Organizationを運営する新しいOS
AI Office、AI Office Building、Agent HR OSを、AIツール群ではなくAI社員を運営する一つのスタックとして捉え直す
企業AIは、孤立した補助ツールから管理されたAI労働へ進みつつある。本稿は、AI Officeが仕事場を、AI Office Buildingが組織トポロジーを、Agent HR OSが採用・評価・昇進・統治の人事レイヤーを担うという全体像を整理し、Human + AI Organization の運営スタックとして解説する。
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 use internal simulations to illustrate how a 30/70 human-agent ratio can preserve responsibility coverage while reducing decision latency versus full human review.