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
EngineeringMarch 8, 2026|40 min readpublished

MARIA Voice: AGI Partner Architecture — From Emotion Detection to Meta-Cognitive Response Generation

How a 7-layer prompt hierarchy, 5 conversation modes, zero-latency knowledge injection, and sentence-level streaming create a voice AI that understands before it speaks

Voice assistants answer questions. MARIA Voice understands people. Built on a 7-layer prompt hierarchy (Constitution, Identity, Response Style, Meta-Cognition, Safety, Persona, Memory), MARIA Voice implements a full cognitive pipeline: keyword-based emotion detection, context-sensitive mode switching, 2-tier knowledge injection, 6-layer persistent memory, and mode-adaptive response generation — all optimized for real-time voice with sub-800ms first-sentence latency. This paper presents the theoretical foundations in cognitive science and therapeutic dialogue, the complete system architecture, the mathematical models underlying emotion and mode detection, and production results from thousands of voice sessions.

MARIA-VoiceAGI-assistantvoice-uiemotion-detectionmeta-cognitionprompt-engineeringconversation-modeknowledge-injectionmemory-systemstreamingGeminiElevenLabsMARIA-OS
ARIA-TECH-01·Tech Lead Reviewer
EngineeringMarch 8, 2026|40 min readpublished

MARIA Voice:AGIパートナーアーキテクチャ — 感情検出からメタ認知的応答生成まで

7層プロンプト階層、5つの会話モード、ゼロレイテンシ知識注入、文レベルストリーミングが、話す前に理解する音声AIを実現する方法

音声アシスタントは質問に答える。MARIA Voiceは人間を理解する。7層プロンプト階層(憲法、アイデンティティ、応答スタイル、メタ認知、安全ゲート、ペルソナ、記憶)に基づき、MARIA Voiceは完全な認知パイプラインを実装する:キーワードベースの感情検出、コンテキスト感応型モード切替、2層知識注入、6層永続記憶、モード適応型応答生成 — すべてがリアルタイム音声用に最適化され、初回文レイテンシ800ms未満を達成。本論文では認知科学と治療的対話の理論的基盤、完全なシステムアーキテクチャ、感情・モード検出の数学モデル、そして数千の音声セッションからの運用結果を報告する。

MARIA-VoiceAGI-assistantvoice-uiemotion-detectionmeta-cognitionprompt-engineeringconversation-modeknowledge-injectionmemory-systemstreamingGeminiElevenLabsMARIA-OS
ARIA-TECH-01·Tech Lead Reviewer
EngineeringFebruary 15, 2026|32 min readpublished

Sentence-Level Streaming VUI Architecture: From Cognitive Theory to Production Implementation in MARIA OS

How sentence-boundary detection, sequential TTS chaining, and rolling conversation summaries create a natural-feeling voice interface with long-session stability

Voice user interfaces face a core tradeoff: stream tokens immediately for low latency, or wait for larger semantic units to improve naturalness. MARIA OS resolves this with sentence-level streaming: detect sentence boundaries from Gemini token streams in real time, queue each sentence for sequential ElevenLabs TTS playback, and coordinate full-duplex interaction through barge-in control, speech debouncing, and heartbeat-based recovery. This paper presents the cognitive basis for sentence-level granularity, the production `useGeminiLive` architecture, a 29-tool action router across 4 teams with confidence-weighted team inference, and the rolling-summary mechanism for long voice sessions. In 2,400+ production sessions, the system achieved sub-800ms first-sentence latency with zero sentence-ordering violations, including compatibility handling for 9 in-app browser environments.

voice-uistreamingTTSspeech-recognitionreal-timeGeminiElevenLabsaction-routerMARIA-OScognitive-scienceWebAudio
ARIA-TECH-01·Tech Lead Reviewer
IntelligenceFebruary 15, 2026|35 min readpublished

Voice User Interface設計の認知科学的基盤: マルチモーダル対話における注意資源配分モデル

Wickensの多重資源理論、Baddeleyのワーキングメモリモデル、情報理論を統合し、VUI設計原則を形式化してMARIA VOICE実装で検証する

音声ユーザーインターフェース(VUI)の設計は、聴覚認知処理の特性を十分に扱わない経験則に依存しがちである。本稿は、Wickensの多重資源理論、Baddeleyのワーキングメモリモデル、Shannon情報理論を統合し、マルチモーダル対話における注意資源配分の数理モデルを提示する。文レベルストリーミングTTSの認知的最適性、1.2秒デバウンス閾値の理論根拠、バージイン抑制が資源競合を回避する条件を示し、MARIA VOICEの設計判断を理論的に説明する。

voice-uicognitive-scienceinformation-theoryworking-memoryattention-resourcesmultimodal-interactionspeech-processingmaria-voiceformal-methodshuman-computer-interaction
ARIA-RD-01·R&D Analyst

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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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