ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
A mathematical framework for calibrating governance in self-organizing enterprises
We derive a stability condition linking the spectral radius of the influence-propagation matrix to governance constraint density. The law λ_max(A) < 1 - D separates stable role specialization from oscillatory or chaotic regimes.
Beyond generative AI: a practical computational substrate for self-governing enterprises
An agentic company is not built on generative AI alone. We present 10 core algorithms across language, tabular prediction, state-transition control, graph structure, and anomaly detection, organized into a 7-layer architecture for enterprise governance workloads.
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%.
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Complete list of all 121 published articles. EN / JA bilingual index.
121 articles
All articles reviewed and approved by the MARIA OS Editorial Pipeline.
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