Effective product schema modeling is fundamental to e-commerce success, enabling accurate product discovery and superior customer experience. However, traditional manual schema modeling processes are severely bottlenecked, producing only tens of attributes per month, which is insufficient for modern e-commerce platforms managing thousands of product types. This paper introduces AttributeForge, the first framework to automate end-to-end product schema modeling using Large Language Models (LLMs). Our key innovation lies in orchestrating 43 specialized LLM agents through strategic workflow patterns to handle the complex interdependencies in schema generation. The framework incorporates two novel components: MC2-Eval, a comprehensive validation system that assesses schemas against technical, business, and customer experience requirements; and AutoFix, an intelligent mechanism that automatically corrects modeling defects through iterative refinement. Deployed in production, AttributeForge achieves an 88× increase in modeling throughput while delivering superior quality: a 59.83% Good-to-Good (G2G) conversion rate compared to 37.50% for manual approaches. This significant improvement in both speed and quality enables e-commerce platforms to rapidly adapt their product schemas to evolving market needs.