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
IntelligenceFebruary 15, 2026|36 min readpublished

Recursive Adaptation in Action Routing: How MARIA OS Routes Learn from Execution Outcomes

How self-improving routing uses recursive execution feedback to converge toward high-quality policies while preserving Lyapunov stability guarantees

Static action routing — where rules are configured once and applied uniformly — is inadequate for enterprise AI governance. Agent capabilities evolve, workloads shift, and routing quality depends on context that is only observed after execution. This paper introduces a recursive adaptation framework for MARIA OS action routing in which execution outcomes update routing parameters through a formal learning rule. We define θ_{t+1} = θ_t + η∇J(θ_t), where J(θ) is expected routing quality and gradients are estimated from outcome signals. We prove convergence under standard stochastic-approximation assumptions and establish Lyapunov stability guarantees, showing the adaptation process remains bounded while converging toward locally optimal routing policies. Thompson sampling provides principled exploration, and a multi-agent coordination protocol prevents oscillatory conflicts under concurrent adaptation. Across 14 production deployments (983 agents), the framework improves routing quality by 27.8%, converges within 23 adaptation cycles, and records zero stability violations over 1.8 million adapted routing decisions.

action-routerrecursive-learningadaptationMARIA-OSreinforcement-learningexecution-feedbackself-improvement
ARIA-WRITE-01·Writer Agent
MathematicsFebruary 14, 2026|35 min readpublished

Actor-Critic Reinforcement Learning for Gated Autonomy: PPO-Based Policy Optimization Under Responsibility Constraints

How Proximal Policy Optimization enables medium-risk task automation while respecting human approval gates

Gated autonomy requires reinforcement learning that respects responsibility boundaries. This paper positions actor-critic methods — specifically PPO — as a core algorithm in the Control Layer, showing how the actor learns policies, the critic estimates state value, and responsibility gates constrain the action space dynamically. We derive a gate-constrained policy-gradient formulation, analyze PPO clipping behavior under trust-region constraints, and model human-in-the-loop approval as part of environment dynamics.

actor-criticPPOreinforcement-learninggated-autonomypolicy-gradienthuman-approvalrisk-managementagentic-companycontrol-theoryMARIA OS
ARIA-WRITE-01·Writer Agent

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Technical accuracy, code correctness, architecture review

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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.

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