RRC-AI Starship Control Plane: Self-Constructing Governance Architecture for Autonomous Intelligent Systems — MSI-2026-005 Technical Report with Full Reconstruction Appendices

We present the RRC-AI Starship Control Plane, a self-constructing governance architecture for autonomous

intelligence systems operating at civilization scale. The system introduces a novel architectural pattern in which

autonomous Python daemons iteratively build, test, and bind governance surfaces without human intervention,

producing 60,000 lines of validated architecture across 462 files in five days of continuous operation. The

architecture governs a frozen codebase of 490 million lines (the Life Intelligence System V3 body) through a

12-subsystem control plane organized into five hierarchical layers: operator/world, cockpit/control room,

control-plane core, mechanical support, and body/subsystem.

Three novel architectural contributions emerge from this work. First, a daemon-driven construction pattern in which

export tools read upstream governance surfaces, produce derived JSON and Markdown readmodels, and trigger

downstream regeneration waves, creating a self-propagating build chain that maintains cross-surface consistency

without centralized orchestration. Seven autonomous daemons operate concurrently, achieving sustained build

velocity of 647 lines per hour in steady state and bursts exceeding 17,000 lines per hour during active development.

Second, a 14-layer operator guidance depth system that models not only data presentation but operator attention,

focus, intent, and agency, including governed takeover semantics where the system validates readiness conditions

before permitting operator intervention. Third, a preGIT dual-truth preservation system (gCgL) that maintains

timestamped architectural snapshots as both disaster recovery points and future seed data for a planned semantic

version control layer (gitL) bridging machine-scale truth with human-auditable Git history.

The control plane implements a closed governance cycle (verdict-to-prompt) with a five-stage operator input pipeline

(payload, form, submission, capture, receipt), a spatial operator room with five renderable zones, and 49 cockpit

surfaces readable by both human operators and AI systems simultaneously. We provide complete empirical data on

build velocity, daemon architecture, derivation chain topology, and phase-over-phase growth across 18 recorded

architectural snapshots spanning 26 phases of autonomous construction. The system represents, to our knowledge, the

first documented instance of governance architecture that constructs itself through autonomous daemon processes

while maintaining nuclear-plant-grade safety invariants.