AI Agents-as-Judge: Automated Assessment of Accuracy, Consistency, Completeness and Clarity for Enterprise Documents (2506.22485v1)

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.

• The paper presents a modular multi-agent framework that automates enterprise document reviews, achieving 95% agreement with human reviewers.
• The system leverages tools like LangChain, CrewAI, TruLens, and Guidance to ensure structured outputs, parallel evaluation, and bias reduction.
• The experimental results show a 12x improvement in review speed over human methods while maintaining high accuracy and minimizing error flags.

Automated Assessment of Enterprise Documents with AI Agents-as-Judge

This paper explores the development and evaluation of a system for automated document review using AI agents specifically designed for structured enterprise documents. The focus is on evaluating the potential of AI agents to perform tasks traditionally carried out by human reviewers, ensuring compliance, accuracy, consistency, and clarity.

Problem Definition

The automated assessment system is built to address the inefficiency of manual document review processes in enterprises. Enterprises possess a variety of highly structured documents such as regulatory filings and internal procedures, which need meticulous evaluation. The primary objective is to determine the feasibility of relying on AI agents to accurately assess such documents, which demands adherence to strict formats and domain-specific terminology.

Objectives of the Study

1. Evaluate AI Capabilities: The paper examines whether AI agents can effectively review business documents, focusing on template matching, factual accuracy, and appropriate terminology usage.
2. Flexible Review System: Constructs a modular framework using contemporary tools such as LangChain, CrewAI, TruLens, and Guidance, allowing rapid adaptation to varying document structures and quality requirements.
3. Comparison with Human Reviewers: The paper benchmarks the efficacy and speed of AI-driven reviews against human performance to delineate areas where AI excels and where human oversight remains necessary.
4. Practical Implementation: The paper aims to provide a straightforward guide for deploying AI agents for document reviews, encapsulating query formulation, response organization, and iterative process enhancement.

Significance of the Research

The research demonstrates that AI agents can significantly reduce the time and human labor involved in document reviews while minimizing errors. AI systems offer consistent performance devoid of personal bias, crucial for maintaining fairness in situations like regulatory checks. The paper also establishes a reusable AI system applicable across various document forms, detailing areas requiring human expertise for full assurance, particularly with complex documents.

System and Methodology

Novel Contributions

The proposal introduces a multi-agent pipeline tailored for enterprise documents, focusing on section-by-section evaluation:

• Multi-Agent Architecture: The system employs specialized agents for various review tasks, enabling parallel evaluation, improving accuracy and efficiency.
• Orchestration Frameworks: Utilizes adaptive frameworks that seamlessly integrate with evolving enterprise needs.
• Structured Outputs: Ensures machine-readable results standardized for downstream processing.
• Continuous Monitoring: Implements feedback loops for iterative improvements and bias reduction.
• Scalability: Demonstrates capacity for bulk document handling, surpassing manual review in speed and consistency.

Implementation Tools

• LangChain: Facilitates document segmentation and process orchestration.
• CrewAI: Distributes tasks among specialized agents, akin to an expert team.
• TruLens: Monitors reviews via dashboards, ensuring quality and bias checks.
• Guidance: Enforces standardized, structured output for easy auditing and analytics.

Experimental Results

Quantitative evaluation against a benchmark of 50 business documents reveals:

• Efficiency: AI-driven reviews are faster (12x improvement) than human reviews.
• Agreement with Humans: High agreement rate (95%) with human evaluations.
• Error and Bias Reduction: Lower error and bias flags compared to manual methods.

Limitations and Future Work

The system faces challenges with high computational costs for top-tier LLMs when processing vast document sets. Occasional false negatives or positives suggest the need for ongoing query customization. Future research will focus on expanding LLM capabilities, improving template-specific customizations, and refining fact-checking mechanisms.

Conclusion

The research shows that AI agents, when orchestrated via a robust framework, can effectively automate the review of structured enterprise documents, offering accuracy and consistency with reduced human resource investment. The approach is adaptable to various document types and industries, with ongoing advancements in AI expected to enhance capabilities further.