Hierarchical AI agent systems present unique permission management challenges. When parent agents spawn children, and those children spawn further descendants, traditional permission models fail to provide graduated capability restriction. Existing approaches treat spawn depth as a termination condition rather than a policy input, offering only binary controls without graduated trust levels. This paper presents a depth-based permission management approach implementing progressive restriction---permissions automatically decrease as spawn depth increases. The system defines multiple permission tiers mapped to generational depth ranges, where each tier permits a specific subset of operations across five categories: agent creation, external communication, persistent storage, computational operations, and administrative functions. The approach achieves automatic least privilege enforcement without requiring explicit per-agent configuration. We describe the conceptual architecture, security properties, and design principles that enable scalable permission management in multi-agent systems with arbitrary hierarchy depths. The progressive restriction model provides strong containment guarantees, defense in depth through multiple security layers, and full auditability for security monitoring and compliance verification.
Matias Chenu Melchior (Sun,) studied this question.