Recent work leverages **Large Language Models (LLMs)** to generate executable code for pedagogical animations using libraries such as **Manim**. However, ensuring spatial correctness and visual legibility remains challenging, as existing frameworks emphasize pedagogical content while overlooking geometric occlusions. We propose the **Symbolic Geometric Agent (SGA)**, a plug-and-play module for code-centric animation pipelines that intercepts LLM-generated code, performs partial execution to extract symbolic scene graphs, and applies targeted refinement when spatial conflicts are detected. We further introduce the **Manim Visual Quality Score (MVQS)**, a deterministic rendering-free proxy for spatial integrity. Experiments on the **MMMC-Code** benchmark across four LLM backbones and two agentic pipelines show that SGA achieves a peak MVQS of **73.11** (**Code2Video + GPT-5.1**), corresponding to a **16.1%** relative improvement over the raw baseline, and improves MVQS in **7 of 8** backbone × pipeline configurations. Additionally, we conduct a human evaluation showing that these improvements translate into human preference, with raters preferring SGA over the raw baseline in **84.4%** of comparisons and over a VLM-based critic in **65.0%**.