Chromatic Continuity as a Sidecar Layer: A Parallel Continuity Plane for AI-Native Infrastructure

Abstract This paper positions Chromatic Continuity as a parallel state sidecar for AI-native infrastructure rather than as a replacement for the existing symbolic stack. Current interoperability protocols such as MCP and A2A address tool access, agent coordination, and transactional exchange, but they do not define a continuous, humane, low-entropy synchronization layer between human presence, edge systems, cloud systems, and infrastructure. Chromatic Continuity is proposed as this missing layer. The central claim is that symbolic systems should continue to handle explicit content, legal records, transactions, and precise commands, while a parallel chromatic layer carries a lighter persistence class: attention mode, relational condition, transit state, infrastructural stability, environmental relevance, and change gradients. In this role, Chromatic Continuity functions as a continuity sidecar: a parallel continuity plane running beside existing rails. The paper argues that this sidecar model is technically plausible because new infrastructures rarely arrive as complete replacements. They first appear as coordination layers, then as persistent background layers, and only later as primary architectures. A phased integration path is therefore proposed: on-device chromatic state layers, local edge broadcast across buildings and wearables, symbolic protocols with chromatic side-channels, and finally infrastructure capable of emitting continuous public chromatic state. To make the model operational, the paper defines a minimal chromatic state vector including hue-domain, intensity-force, transition-drift, resonance-geometry, stability-span, and modulation-mode. It also identifies four requirements for maturation into a viable standard: a bounded state vector, an authenticity model, a privacy boundary, and accessibility/fallback mechanisms. Concrete landing zones are proposed in retail, transit, hospitals, campuses, vehicles, and wearables. The paper concludes that MCP and A2A solve interoperability, while Chromatic Continuity addresses humane continuity. In its first deployable form, CC-1 should therefore be understood as a non-extractive continuity sidecar for AI-native systems: a parallel field layer preserving coherence without symbolic identity capture.