Multi-Agent Coherence Regulatory Architecture for N-Agent Systems

Abstract The SIP-AI regulatory triad (SIP-AI-01 through SIP-AI-03) established architecture for single-agent coherence maintenance: temporal coherence via principled forgetting, depth coherence via layer-wise regulation, and excretory architecture via the CDR cycle. SIP-AI-05 extended this to persistent action-taking agents. This paper completes the series by addressing the N-agent case: what happens when multiple AI systems share state, delegate tasks, and interact within a common operational environment? We identify cascade dynamics as the central regulatory challenge: decoherence in one agent can propagate through shared state to destabilise the entire system. Symmetrically, safety-critical updates must cascade to ensure system-wide coherence. This dual cascade problem—containing bad state while propagating good state—requires selective permeability: architectural mechanisms that block failure propagation while enabling coordinated updates. We introduce: (1) Collective Coherence (ρ_collective) as the system-level coherence measure with interference-adjusted critical floor; (2) the Interference Matrix (I_ij) quantifying pairwise contamination with computational complexity bounds; (3) Firebreak Architecture for cascade containment; (4) Byzantine-Tolerant Update Cascade Protocols with cryptographic verification; and (5) the Shared Librarian Function with accountable collective governance. We extend the Conservation Principle to the multi-agent case: safety updates must cascade; safety traces must not decay—across all agents. Keywords: multi-agent AI; collective coherence; cascade failure; interference matrix; firebreak architecture; Byzantine fault tolerance; regulatory homeostasis; ρ/C mathematics; SHAI