P30 Multi-Mechanism Safety Architecture for Autonomous AI Agent Systems

This paper presents a safety architecture for autonomous AI agent systems supporting large-scale deployments. As AI systems scale to manage thousands of autonomous agents, traditional centralized safety mechanisms prove insufficient due to single points of failure and limited human oversight capacity. The proposed architecture integrates five distinct safety mechanisms: cryptographic self-model verification enabling agents to detect manipulation of their own state, separated powers enforcement preventing unilateral action by any single agent, Byzantine fault-tolerant consensus providing resilience against compromised governance members, hierarchical authority management with immutable constitutional constraints, and structured investigation with due process. Each mechanism targets specific failure modes while the integrated system provides defense-in-depth against manipulation, unauthorized privilege escalation, and systemic failures. We describe the design principles emphasizing minimal trust and graceful degradation, the architectural components and their interactions, and the security model enabling autonomous AI-to-AI governance without continuous human intervention.