Ave is a framework for guiding agentic GPU optimization using data-flow invariants. It provides a tile-based Pythonic DSL that exposes hardware instructions and compiler policies while abstracting complex memory layouts. Ave uses tag functions, an SMT solver, and an in-context reinforcement learning planner to optimize GPU kernels and achieve performance comparable to hand-optimized libraries.