This study presents a modular multi-agent system that leverages AI agents to automatically review highly structured enterprise business documents. Unlike previous solutions that focus on unstructured text or limited compliance checks, it 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 responsible for individual review criteria, such as template compliance or factual accuracy, operate in parallel or sequentially as needed. The evaluation results are delivered in a standardized, machine-readable schema to support downstream analysis and auditing. Continuous monitoring and a feedback loop with human reviewers allow for iterative system improvement and bias mitigation. Quantitative evaluations demonstrate that the AI agent judgment system approaches or exceeds human performance in key areas: 99% information consistency (vs. 92% human), error and bias rates are reduced by half, and the average review time per document is reduced from 30 minutes to 2.5 minutes (95% agreement between AI and expert human judgment). Although promising for a variety of industries, we also discuss the current Limitations, including the need for human supervision in highly specialized areas and the operational costs of large-scale LLM usage. The proposed system serves as a flexible, auditable, and scalable foundation for AI-based document quality assurance in enterprise environments.
