TAG ARCHIVE
runtime-governance
8 MARIA OS blog articles tagged runtime-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.
Applications Maintained by Dynamic Harness-Driven Development
A general operating model for collecting runtime evidence, planning repairs, and keeping AI-assisted products stable
This application is maintained through dynamic harness-driven development. The method treats harness results as operational evidence, converts failures into bounded repair plans, and preserves learning without exposing internal implementation details.
動的ハーネス駆動開発により保守されるアプリケーション
Runtime evidenceを収集し、改修計画へ変換し、AI支援プロダクトを安定運用するための汎用モデル
このアプリは動的ハーネス駆動開発により保守されています。Harness結果を運用証跡として扱い、失敗を境界付きの改修計画へ変換し、内部実装の詳細を公開せずに学習を残す方法です。
Harness-Driven Development: Building Agentic Systems from Runtime Evidence Backward
A development method where scenarios, gates, scorecards, and repair boundaries are designed before implementation
Harness-driven development treats the dynamic harness as the primary specification. Instead of writing agent code first and testing it later, teams define runtime episodes, failure taxonomies, gates, and evidence contracts first, then let implementation converge toward measurable behavior.
ハーネス駆動開発：Runtime Evidenceから逆算してAgentic Systemを作る
実装より先にscenario、gate、scorecard、repair boundaryを設計する開発方法論
ハーネス駆動開発では、dynamic harnessをテスト補助ではなく主仕様として扱う。promptやtoolを書く前に、runtime episode、failure taxonomy、scorecard、authority boundaryを定義し、実装を測定可能な振る舞いへ収束させる。
MARIA Self-Healing Runtime: Safe Autonomous Repair for Agentic Systems
A Self-Evolving Harness Runtime design for failure analysis, patch planning, scoped fixing, cross-cutting replay, memory-driven prevention, and human approval
MARIA Self-Healing Runtime is the safety-first repair layer inside MARIA OS. It observes failures, diagnoses root causes, plans bounded repairs, creates reviewable PRs, replays cross-cutting evidence, learns prevention patterns, and keeps human authority over high-risk change.
MARIA Self-Healing Runtime：Agentic Systemの安全な自律改修基盤
Failure Analyzer、Meta-Harness、Envelope、Memory Store、Human Approval Gate、Loop Controlで自己修復を統治する
MARIA Self-Healing Runtimeは、MARIA OS内部の安全第一の改修runtimeである。失敗を検知し、原因を分析し、境界付き改修を計画し、review可能なPRを作り、横断Harnessで再検証し、再発防止をMemory化しながら、高リスク変更の最終責任を人間に戻す。
Dynamic Harness and Phase-Space Control: From virtual-talent to MARIA OS
Reframing runtime episodes, failure taxonomies, dynamic scorecards, repair proposals, and controlled self-healing as phase control for agentic society
The central question for agentic systems is shifting from model intelligence to runtime phase control. This article defines the Dynamic Harness as a Runtime Governance Layer that observes, evaluates, and controls the phase space of an agent runtime, connecting MARIA OS research with implementation lessons from bonginkan/virtual-talent.
動的ハーネスと位相空間制御：virtual-talentからMARIA OSへ
runtime episode、failure taxonomy、dynamic scorecard、repair proposal、controlled self-healingを、Agentic Society Runtimeの位相制御として再定義する
AI Agentの時代における本質的な問いは、モデルがどれほど賢いかではなく、知能がどの位相に入り、どの位相から戻れなくなるかである。本稿は、bonginkan/virtual-talentのProducer AIで進むDynamic Harness実装を踏まえ、MARIA OSにおけるハーネスをRuntime Governance Layer、さらにAgent runtimeの位相空間を制御する層として定義する。runtime episode、failure taxonomy、dynamic scorecard、repair proposal、controlled self-healingを軸に、静的テストから動的制御へ移行する設計原理を整理し、企業OSとAgentic Societyへ拡張する研究課題を示す。