Each year, college admissions offices face an overwhelming challenge: processing millions of high school transcripts, each with unique formats, grading systems, and layouts. This manual process creates operational bottlenecks that delay admissions decisions and consume valuable resources. We present a transformative solution through a multi-agent AI system where specialized agents collaborate to automatically process diverse transcript formats through intelligent coordination and communication. Our multi-agent architecture consists of three specialized agents-a Pattern Recognition Agent for format-specific parsing, a Semantic Analysis Agent for natural language understanding, and a Vision Intelligence Agent for multimodal document analysis-coordinated by an Orchestration Agent that manages agent communication and result reconciliation. Our key innovation lies in agent-based quality control using GPA extraction as a coordination signal, ensuring reliable agent collaboration and preventing critical information loss. When evaluated on 40 real world transcripts from high schools across 13 U.S. states, our agent system successfully processed every document, achieving 96.7% accuracy compared to expert manual review while maintaining practical processing speeds of 45 seconds per transcript. This work demonstrates how multi-agent coordination can solve complex document processing challenges, offering institutions a scalable, collaborative AI solution that preserves accuracy while dramatically reducing processing time.

翻译：每年，大学招生办公室都面临一项艰巨挑战：处理数百万份高中成绩单，每份成绩单都有独特的格式、评分系统和布局。这一人工流程造成了操作瓶颈，延迟了招生决定，并消耗了宝贵的资源。我们通过一个多智能体AI系统提出了一种革命性解决方案，其中专业智能体通过智能协调与通信协作，自动处理多样化的成绩单格式。我们的多智能体架构包括三个专业智能体——模式识别智能体（用于特定格式的解析）、语义分析智能体（用于自然语言理解）以及视觉智能体（用于多模态文档分析），并配备一个编排智能体来协调智能体通信和结果整合。我们的关键创新在于基于智能体的质量控制，使用GPA提取作为协调信号，确保可靠的智能体协作并防止关键信息丢失。在来自美国13个州高中40份真实成绩单的评估中，我们的智能体系统成功处理了每一份文档，与专家人工评审相比达到了96.7%的准确率，同时保持了每份成绩单45秒的实用处理速度。这项工作展示了多智能体协调如何解决复杂的文档处理挑战，为机构提供了一种可扩展的协作式AI解决方案，在显著缩短处理时间的同时保持准确性。