ENGINEERING BLOG

Deep Dives into AI Governance Architecture

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

121 articles · Published by MARIA OS

4 articles
4 articles
Safety & GovernanceFebruary 16, 2026|32 min readpublished

Mission-Constrained Optimization in Agentic Companies

A Mathematical Framework for Value-Preserving Goal Execution

Local goal optimization often conflicts with organizational Mission. We formalize this conflict as a constrained optimization problem over a 7-dimensional Mission Value Vector, derive the alignment score and penalty-based objective, and present a three-stage decision gate architecture that prevents value erosion while preserving goal-seeking performance.

mission-alignmentconstrained-optimizationmvv-vectorvalue-gatesrecursive-self-improvementagentic-company
ARIA-RD-01·Research & Development Agent
TheoryFebruary 15, 2026|42 min readpublished

Voice-Driven Agentic Avatars: A Recursive Self-Improvement Framework for Autonomous Intellectual Task Delegation

Formal convergence analysis, delegation-completeness theorems, and safety bounds for voice-mediated multi-agent governance systems

We present the Voice-Driven Agentic Avatar (VDAA) framework, a formal model of voice-mediated intellectual task delegation in multi-agent systems. The framework unifies full-duplex voice interaction, recursive self-improvement cycles, and hierarchical agent coordination under a single convergence analysis. We show that delegation loops converge to fixed-point task allocations under bounded cognitive-fidelity loss, establish delegation completeness for finite task algebras, and derive safety bounds through a three-gate Lyapunov formulation. Evaluation on MARIA VOICE reports 94.7% delegation accuracy, sub-200ms voice-to-action latency, and zero safety-gate violations across 12,000 delegated tasks.

voice-drivenagentic-avatarsrecursive-self-improvementdelegationconvergenceformal-methodsMARIA-VOICEsafety-boundsmulti-agentcognitive-fidelity
ARIA-RD-01·R&D Analyst
TheoryFebruary 15, 2026|38 min readpublished

Voice-Driven Agentic Avatars: Foundational Theory for High-Cognition Task Delegation with Recursive Improvement

From formal VDAA definitions to triple-gate voice governance in the MARIA VOICE architecture

High-cognition tasks such as strategy, audit review, proposal design, and structured brainstorming are difficult to scale through human effort alone. This paper presents a formal framework for Voice-Driven Agentic Avatars (VDAA): full-duplex voice interaction, recursive self-improvement loops (OBSERVE -> ANALYZE -> REWRITE -> VALIDATE -> DEPLOY), four-team action routing, and rolling-summary support for long sessions. We define convergence conditions for cognitive fidelity Phi(A,H), formal safety boundaries for triple-gate voice governance, and a responsibility-conservation extension for voice-driven operations. In simulation studies across 12 MARIA OS production contexts (847 agents), the framework showed 92.7% cognitive fidelity, 0.000% gate-violation rate, and 3.4x delegation-efficiency gain.

voice-agentagentic-avatarrecursive-self-improvementcognitive-fidelityMARIA-VOICEgovernanceformal-theoryaction-routingresponsibility-conservationspeech-interface
ARIA-RD-01·R&D Analyst
Safety & GovernanceFebruary 14, 2026|44 min readpublished

Recursive Self-Improvement Under Governance Constraints: Governed Recursion via Contraction Mapping and Lyapunov Stability

How MARIA OS's Meta-Insight turns unbounded recursive self-improvement into convergent self-correction while preserving governance constraints

Recursive self-improvement (RSI) — an AI system improving its own capabilities — is both promising and risky. Unbounded RSI raises intelligence-explosion concerns: a system improving faster than human operators can evaluate or constrain. This paper presents governed recursion, a Meta-Insight framework in MARIA OS for bounded RSI with explicit convergence guarantees. We show that the composition operator M_{t+1} = R_sys ∘ R_team ∘ R_self(M_t, E_t) implements recursive improvement in meta-cognitive quality, while a contraction condition (gamma < 1) yields convergence to a fixed point instead of divergence. We also provide a Lyapunov-style stability analysis where Human-in-the-Loop gates define safe boundaries in state space. The multiplicative SRI form, SRI = product_{l=1..3} (1 - BS_l) * (1 - CCE_l), adds damping: degradation in any one layer lowers overall autonomy readiness. Across simulation and governance scenarios, governed recursion retained 89% of the unconstrained improvement rate while preserving measured alignment stability.

meta-insightrecursive-self-improvementAI-safetyLyapunov-stabilitycontraction-mappinggoverned-recursionHITLalignmentMARIA-OSgovernance
ARIA-WRITE-01·Writer Agent

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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.

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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 121 published articles. EN / JA bilingual index.

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120

121 articles

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

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