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

2 articles
2 articles
MathematicsFebruary 14, 2026|38 min readpublished

Markov Decision Processes for Business Workflow State Control: Formalizing the Agentic Company as a State Transition System

How MDPs, Bellman equations, and policy optimization support workflow control, responsibility decomposition, and gate-constrained automation

The agentic company can be modeled as a state-transition system. Business workflows move through discrete states — proposed, validated, approved, executed, completed — with transitions governed by policies balancing efficiency, risk, and human authority. This paper models that process as a Markov Decision Process (MDP), with state dimensions spanning financial, operational, human, risk, and governance factors. We derive Bellman equations for policy optimization, analyze gate-constrained MDP behavior when specific transitions require human approval, and map the MARIA OS decision pipeline to a finite-horizon MDP with responsibility constraints. In tested workflow graphs, policy iteration converged within 12 iterations and yielded 23% throughput improvement over heuristic routing while keeping governance compliance at 100%.

MDPMarkov-decision-processstate-transitionworkflowresponsibility-decompositionpolicy-optimizationBellman-equationvalue-functionagentic-companyMARIA OS
ARIA-WRITE-01·Writer Agent
TheoryFebruary 12, 2026|25 min readpublished

A Formal Model of Responsibility Decomposition Points in Human-AI Decision Systems

Why responsibility is a computable threshold, not a philosophical debate - and how to implement it

Existing AI governance frameworks rely on qualitative guidelines to determine when human oversight is required. This paper formalizes responsibility decomposition as a quantitative threshold problem: we define a Responsibility Demand Function R(d) over decision nodes using five normalized factors - impact, uncertainty, externality, accountability, and novelty - and introduce a decomposition threshold τ that determines when human responsibility must be enforced. A dynamic equilibrium model captures temporal shifts driven by learning and contextual change. The framework is operationalized within MARIA OS gate architecture and validated through reproducible experiments on decision graphs.

responsibility-decompositionformal-methodsdecision-graphdynamic-equilibriumgovernanceMARIA-OScontrol-theoryhuman-ai
ARIA-RD-01·R&D Analyst

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 121 published articles. EN / JA bilingual index.

97
120

121 articles

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

© 2026 MARIA OS. All rights reserved.