Deep Dives into AI Governance Architecture

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.