AI Agents-as-Judge: Automated Assessment of Accuracy, Consistency, Completeness and Clarity for Enterprise Documents

TL;DR

This paper introduces a multi-agent AI system for automated, section-by-section review of enterprise documents, achieving high accuracy, consistency, and efficiency, surpassing human performance in key metrics.

Contribution

The study presents a novel modular multi-agent framework utilizing modern orchestration tools for comprehensive enterprise document assessment, with standardized outputs and iterative human-in-the-loop improvements.

Findings

Achieves 99% information consistency, surpassing 92% of humans.

Halves error and bias rates compared to manual reviews.

Reduces review time from 30 to 2.5 minutes per document.

Abstract

This study presents a modular, multi-agent system for the automated review of highly structured enterprise business documents using AI agents. Unlike prior solutions focused on unstructured texts or limited compliance checks, this framework leverages modern orchestration tools such as LangChain, CrewAI, TruLens, and Guidance to enable section-by-section evaluation of documents for accuracy, consistency, completeness, and clarity. Specialized agents, each responsible for discrete review criteria such as template compliance or factual correctness, operate in parallel or sequence as required. Evaluation outputs are enforced to a standardized, machine-readable schema, supporting downstream analytics and auditability. Continuous monitoring and a feedback loop with human reviewers allow for iterative system improvement and bias mitigation. Quantitative evaluation demonstrates that the AI Agent-as-Judge system approaches or exceeds human performance in key areas: achieving 99% information consistency (vs. 92% for humans), halving error and bias rates, and reducing average review time from 30 to 2.5 minutes per document, with a 95% agreement rate between AI and expert human judgment. While promising for a wide range of industries, the study also discusses current limitations, including the need for human oversight in highly specialized domains and the operational cost of large-scale LLM usage. The proposed system serves as a flexible, auditable, and scalable foundation for AI-driven document quality assurance in the enterprise context.