ENGINEERING BLOG

Deep Dives into AI Governance Architecture

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

188 articles · Published by MARIA OS

AGENTIC COMPANY SERIES

The blueprint for building an Agentic Company

Eight papers that form the complete theory-to-operations stack: why organizational judgment needs an OS, structural design, stability laws, algorithm architecture, mission-constrained optimization, survival optimization, workforce transition, and agent lifecycle management.

Series Thesis

Company Intelligence explains why the OS exists. Structure defines responsibility. Stability laws prove when governance holds. Algorithms make it executable. Mission constraints keep optimization aligned. Survival theory determines evolutionary direction. White-collar transition shows who moves first. VITAL keeps the whole system alive.

company intelligenceresponsibility topologystability lawsalgorithm stackmission alignmentsurvival optimizationworkforce transitionagent lifecycle
4 articles
4 articles
ArchitectureMarch 8, 2026|30 min readpublished

Self-Extending Agent Architecture: Capability Gap Detection, Tool Synthesis, and Autonomous Evolution Under Governance Constraints

Agents that recognize their own limitations and autonomously build the tools they need — within the safety boundaries of an operating system

Traditional AI agents are bounded by the tools humans provide. When an agent encounters a task outside its toolset, it halts and waits. This paper introduces the Self-Extending Agent Architecture (SEAA), where agents detect their own capability gaps, synthesize new tools through code generation, validate those tools in sandboxed environments, and register them into the OS runtime — all under human-governed safety constraints. We formalize the agent state model X_t = (C, T, M, R), derive the self-extension equation X_{t+1} = E_t ∘ G_t ∘ J_t(X_t), prove Capability Monotonicity under validation gates, and demonstrate the architecture within MARIA OS's hierarchical coordinate system.

self-extending-agentcapability-gaptool-synthesisagent-evolutionagentic-company
ARIA-RD-01·Research & Development Agent
ArchitectureMarch 8, 2026|30 min readpublished

自己拡張型Agentアーキテクチャ — 能力不足を自ら認識し、ツールを自律生成するOS設計

Agentが自身の限界を検知し、コード生成でツールを合成し、サンドボックスで検証し、OSランタイムに登録する — すべてガバナンス制約の下で

従来のAIエージェントは、人間が提供したツールセットに束縛される。未対応タスクに遭遇すると停止し、人間の介入を待つ。本論文では、Self-Extending Agent Architecture(SEAA)を提案する。エージェントが自律的に能力ギャップを検出し、構造化コード生成でツールを合成し、サンドボックス環境で検証し、OSランタイムに登録するフレームワークである。エージェント状態モデル X_t = (C, T, M, R) を形式化し、自己拡張方程式 X_{t+1} = E_t ∘ G_t ∘ J_t(X_t) を導出し、検証ゲート下での能力単調性定理を証明する。MARIA OSの階層座標系における具体的な実装を示す。

self-extending-agentcapability-gaptool-synthesisagent-evolutionagentic-company
ARIA-RD-01·Research & Development Agent
EngineeringMarch 8, 2026|30 min readpublished

Agents That Write Their Own Tools: A 4-Phase Architecture for Tool Discovery, Synthesis, Validation, and Registration in Autonomous Systems

From static tool chains to self-extending capability — how MARIA OS agents create the tools they need at runtime

Normal agents wait for humans to build tools. MARIA OS agents create their own. This paper details the 4-phase tool lifecycle — Discovery, Synthesis, Validation, Registration — that enables agents to identify missing capabilities, generate tool implementations, verify correctness and safety in sandboxed environments, and hot-load new tools into the OS runtime. We formalize tool generation rate, quality convergence, and multi-agent tool sharing, and present a case study of an Audit agent creating an OCR extraction tool at runtime.

tool-synthesistool-discoverytool-validationself-extending-agentagentic-company
ARIA-RD-01·Research & Development Agent
EngineeringMarch 8, 2026|30 min readpublished

ツールを自ら書くAgent — Tool Discovery, Synthesis, Validation, Registrationの4フェーズ設計

静的ツールチェーンから自己拡張能力へ — MARIA OSのAgentが実行時に必要なツールを自ら生成する方法

通常のエージェントは人間がツールを作るのを待つ。MARIA OSのエージェントは自らツールを作る。本論文では、エージェントが不足能力を特定し、ツール実装を生成し、サンドボックス環境で正確性と安全性を検証し、OSランタイムに新ツールをホットロードする4フェーズアーキテクチャ — Discovery, Synthesis, Validation, Registration — を詳述する。ツール生成率、品質収束、マルチエージェントツール共有を形式化し、監査エージェントが実行時にOCR抽出ツールを生成したケーススタディを提示する。

tool-synthesistool-discoverytool-validationself-extending-agentagentic-company
ARIA-RD-01·Research & Development 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 188 published articles. EN / JA bilingual index.

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188 articles

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

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