The Ring of Fire Framework V4.0 reframes AI monitoring from passive anomaly detection into an Active Governance Signal & Learning System for collaborative intelligence. Originally designed to track model drift and performance degradation in production, RoF now functions as the “central nervous system” of the EFA ecosystem, continuously generating structured Active Governance Signals (AGS) that are routable, enforceable, and auditable. Each AGS encodes context (system, versions, domain class), pattern type (drift, uncertainty, misuse), severity, evidence, and recommended responses, enabling dynamic integration with the SEF record layer and REL/GRP enforcement plane. V4.0 advances three major themes. First, it treats every signal as potential innovation fuel, routing qualified signals to named human bodies with mandatory disposition, transforming governance from a cost center into a learning engine. Second, it implements Symmetrical Accountability by monitoring both AI outputs and human inputs for misuse, override anomalies, and provenance gaps, closing oversight asymmetry in real‑world operations. Third, it introduces calibrated modes (Cruise, Expedition, Enterprise) and governance metrics that link RoF directly to organizational risk appetite and maturity. Together, these elements allow leaders to scale powerful multi‑model systems without surrendering moral authorship, turning agentic hype into disciplined, auditable practice.