ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
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%.
Defining code changes as state transitions with reproducibility guarantees and gate-enforced approval workflows
AI code generation is probabilistic, so the same prompt may produce different outputs across runs. In enterprise systems, this requires reproducibility, auditability, and explicit approval controls for every change. This paper introduces DB-Approved Development, a framework that models code changes as database-backed state transitions with reproducibility guarantees and gate-enforced approval workflows for AI-generated code.
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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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