IDP AutoOpt: Agent-Driven Optimization of Document Processing Pipeline Configurations

2026-07-13

AI Summary

IDP AutoOpt使用自主LLM代理优化文档处理流程配置，通过闭环运行和领域技能指导，解决了手动调优耗时且难以扩展的问题，提高了准确性和效率。

Abstract

We present IDP AutoOpt, an autonomous LLM agent that discovers high-performing configurations for intelligent document processing (IDP) pipelines. Tuning IDP prompts, models, OCR settings, and schemas jointly currently costs domain specialists 20 to 80+ person-hours per document type and does not scale as enterprises add document classes. IDP AutoOpt runs a closed loop: it scores a configuration on a small labeled set, diagnoses field-level errors, generates targeted edits, and re-evaluates, guided by human-authored domain skills that encode production expertise. Across extraction, classification, and packet-splitting tasks deployed in healthcare, marketing-intelligence, and financial-services settings, IDP AutoOpt matches or exceeds human-expert accuracy at equal or lower cost (on an extraction benchmark, 90.2% vs 81.6% at 4.6 x lower per-page cost), cutting configuration time from weeks to under two hours. We further show that agent LLM capability has a hard threshold below which optimization fails, and that curated domain skills outperform raw source-code access, which can degrade performance when provided without structure. We also share practical lessons on context management and variance mitigation. Requiring only a configurable pipeline, a scoring function, and a small labeled set, the approach extends beyond IDP to other enterprise AI systems, such as RAG and multi-agent workflows, where configuration bottlenecks deployment.

Problem

• intelligent document processing
• configuration tuning
• domain specialists
• scalability
• cost efficiency

Innovation

• autonomous LLM agent
• intelligent document processing (IDP)
• closed loop optimization
• domain skills
• enterprise AI systems