operator-composition

Structural Architecture of Meta-Insight: Three-Layer Meta-Cognitive Decomposition Aligned with Organizational Hierarchy

Why meta-cognition in multi-agent systems should be decomposed by organizational scope, and how MARIA coordinates provide natural reflection boundaries

Meta-cognition in autonomous AI systems is often modeled as a monolithic self-monitoring layer. This paper argues that monolithic designs are structurally weak for multi-agent governance and introduces a three-layer architecture (Individual, Collective, System) that decomposes reflection by organizational scope. We map these layers to MARIA coordinates: Agent, Zone, and Galaxy. The update operator M_{t+1} = R_sys ∘ R_team ∘ R_self(M_t, E_t) forms a contraction under Banach fixed-point conditions when layer operators are Lipschitz-bounded, yielding convergence to a stable meta-cognitive equilibrium. We also show how scope constraints bound self-reference depth and mitigate infinite-regress failure modes. Across 12 MARIA OS deployments (847 agents), this architecture reduced collective blind spots by 34.2% and improved organizational learning rate by 2.1x versus flat baselines.