Commercial design platforms increasingly edit documents through large language model (LLM) agents, but two practical problems block reliable deployment: legacy document formats expose only flat, absolutely positioned elements, so agents must recompute coordinates and routinely break layouts; and design has no unique ground truth, so diff-against-reference metrics penalize valid-but-different outputs. We present ACE, an agentic canvas editor over a hierarchical scene-graph with a presentation-specialized action space (98 tools), paired with CARE, a content-aware router that feeds the agent only the relevant slice of each deck (avg. ∼ 89% input-token reduction), and a self-correction loop driven by a ground-truth-free instruction-following (IF) judge whose natural-language critique is fed back as the next-turn instruction. On a multi-slide benchmark and with a fixed backbone, a scene-graph editor in a single turn already matches a same-backbone agentic HTML pipeline that iterates internally; adding self-correction lifts ACE clearly above it on quality (IF 4.45 vs. 4.09; VQ 4.02 vs. 3.89) while remaining 1.75× faster and ∼ 44% cheaper. Self-correction contributes +0.41 IF / +0.27 VQ, and 66% of cases halt after one pass, keeping it cheap.