Artificial superintelligence poses unprecedented safety challenges. One critical concern is AI containment ensuring a powerful rogue AI system cannot cause harm or escape human control. This paper introduces Swarm-Mesh, a novel decentralized containment framework inspired by swarm intelligence and biological quorum-sensing mechanisms. We review current AI risk scenarios and containment proposals (including emergency “kill switches,” AI boxing, and containment firewalls) and examine their limitations. Drawing on swarm intelligence theory, we develop a multi-agent architecture in which numerous simple watchdog agents collectively monitor and constrain a potentially unaligned AI. We present a theoretical framework for Swarm-Mesh based on quorum sensing and resilient consensus: no single agent can unilaterally fail or be compromised without detection by others, and a quorum threshold of agreeing agents is required to trigger containment actions. The Swarm-Mesh architecture is detailed with hierarchical control layers, agent communication protocols, anomaly-detection algorithms, and redundant fail-safes designed to resist tampering. We provide formal models, including quorum threshold equations, probabilistic alert propagation dynamics, trust decay functions for unreliable agents, and a consensus model for containment decisions. An evaluation strategy is outlined for testing Swarm-Mesh in simulated environments, measuring its ability to detect and stop misbehaving AI in real time. We also discuss ethical and policy implications, such as global governance for mandated containment, potential surveillance and misuse risks of such a system, and moral trade-offs between safety and innovation. Swarm-Mesh is positioned as a contribution to AI safety research, illustrating how bio-inspired distributed defence mechanisms could enhance our ability to contain advanced AI while rigorous alignment solutions are developed.
J.S Rolle (Sun,) studied this question.