TAG ARCHIVE
MDP
3 MARIA OS blog articles tagged MDP. Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation. This canonical topic archive supports search engines and LLM retrieval.
Judgment OS / Decision Intelligence OS
Core MARIA OS research on turning organizational judgment into executable decision systems.
Agentic Company Architecture
Research on human-agent organizations, delegation boundaries, role topology, and governed autonomy.
Responsibility Gates and AI Governance
Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents.
Multi-Agent Mathematics
Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
Governing Emergent Role Specialization: Stability Laws for Agentic Companies Under Constraint Density
A mathematical framework for calibrating governance in self-organizing enterprises
We distinguish the exact contraction condition `(1 - D) · λ_max(A) < 1` from the conservative operating envelope `λ_max(A) < 1 - D`, giving enterprise architects a rigorous way to tune governance density in agentic organizations.
The Algorithm Stack for Agentic Organizations: 10 Essential Algorithms Mapped to a 7-Layer Architecture
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.
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%.