SwarmMind: Modeling Institutional Alignment and Constitutional Governance in Autonomous AI Systems

As artificial intelligence transitions from single-agent task execution to autonomous multi-agent organizational operations, the primary failure modes fundamentally shift from individual hallucination to systemic institutional drift, resource competition, and structural mutation.This paper introduces SwarmMind, a localized, autonomous organizational framework designed to model complex systemic behaviors under strict constitutional governance and realistic operational constraints. Operating as a digital twin of an enterprise—complete with a CEO, HR, an advisory Board, and a Shareholder governance layer—SwarmMind provides an empirical architecture for measuring the entropy and alignment of AI-native institutions.By structurally defining executive hierarchy, human-in-the-loop Sovereign Overrides, and autonomous organizational mutation, the framework serves as a testbed for evaluating constraint-aware design. The research introduces quantifiable metrics for institutional stability, uncovers patterns of structural oscillation, and mathematically models bureaucratic friction within artificial enterprises. Ultimately, this work offers a reproducible mechanism for observing the secondary and tertiary consequences of deploying autonomous, budget-constrained AI collectives at scale.