EdgeGate — Evidence-Gated Physical AI

EdgeGate is an evidence-gated runtime authorization layer for Physical AI. It addresses a critical deployment problem: perception systems can produce stale, drifting, obstructed, or unsafe observations while downstream AI systems may still attempt to act on them. EdgeGate places a deterministic authorization layer between perception and physical action. Every proposed action is evaluated against runtime evidence including perception confidence, scene freshness, spatial clearance, environmental drift, and restricted-zone constraints. The system produces an explicit runtime decision: PROMOTE, ROLLBACK, or REJECT. Instead of treating model output as automatically executable, EdgeGate requires the proposal to satisfy safety gates before authorization. The prototype includes replayable safety scenarios demonstrating how the same policy responds to fresh perception, stale state, drift, and restricted-zone violations. Each decision generates an evidence ledger containing the observed telemetry, gate results, reasons, and final authorization decision. OpenVINO provides the local CPU edge inference path, while the policy engine remains deterministic and auditable. This architecture is designed for low-latency edge deployment where connectivity, explainability, and predictable failure behavior matter. EdgeGate is a hackathon prototype using synthetic scene telemetry for deterministic policy demonstration. It does not directly control a physical robot and is not a safety certification.