The goal is to support ML feature stewards in evaluating and selecting features for models built over Knowledge Graph data. The workflow must analyze existing features, propose new candidates, assess leakage and temporal validity, and ensure production feasibility under latency and freshness constraints. The final output includes recommendations, monitoring plans, and feature registry updates that are lifecycle-aware and engineering-focused.