企业文档处理团队终于有了可落地的多智能体方案——MADP 用 HITL 机制平衡自动化与准确率，做发票、合同等批量文档处理的团队可以直接参考其部署结果和碳排放数据。
MADP 是一个面向企业文档处理的多智能体架构，包含分类、拆分、解析、提取和验证五个专用智能体，并引入人工在环（HITL）机制和提示微调反馈继承（PFTFI）方法。在10万张发票/年的生产场景中，MADP 可减少约70%的全职人力需求；实际部署955份文档后，全管线自动化率达97%，仅3%需非AI回退。在100份文档的消融测试中，完整配置下文档级准确率达98.5%。相比传统人工处理，MADP 还减少69%的二氧化碳排放、69%的能耗和63%的水耗，并对比了 Granite-Docling、Mistral-Small、DeepSeek-OCR 等多个 LLM 后端。
MADP: A Multi-Agent Pipeline for Sustainable Document Processing with Human-in-the-Loop
Document processing automation remains a critical challenge in enterprise environments, where traditional manual approaches are labor-intensive and error-prone. We present MADP, a multi-agent architecture that addresses the challenge of automating document processing in enterprise settings by combining deep learning-based classification and parsing with large language model extraction, while maintaining accuracy through selective human validation. Our system integrates five specialized agents--Classificator, Splitter, Parser, Extraction, and Validator--with a Human-in-the-Loop (HITL) mechanism and a novel Prompt Fine Tuning with Feedback Inheritance (PFTFI) approach. The operational analysis on a production use-case scenario of 100,000 invoices per year indicates a potential reduction of Full-Time Equivalent (FTE) requirements by approximately 70%. Production deployment on 955 real-world documents processed through January 2026 achieves a 97.0% full-pipeline automation rate, with only 3% requiring non-AI fallback. Ablation evaluation on a stratified 100-document subset (5 documents per each of 20 supplier/document-type categories) demonstrates that the full MADP configuration with Human-in-the-Loop supervision attains 98.5% document-level accuracy. Additionally, we present a comprehensive sustainability analysis showing that our hybrid AI+HITL approach reduces CO2 emissions by 69%, energy consumption by 69%, and water usage by 63% compared to traditional manual processing. Benchmark comparisons of multiple LLM backends (Granite-Docling, Mistral-Small, DeepSeek-OCR) provide practical insights for deployment in production environments.