TAG ARCHIVE
state-transition
2 MARIA OS blog articles tagged state-transition, organized as a Bonginkan topic archive for 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.
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%.
DB-Approved Development: Consistency Proofs for AI-Generated Code Through State Transition Modeling
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.