A Conceptual Framework for Multi-Agent AI Quality Control in The Review of Regulated Documents

Abstract

Organizations in pharmaceutical, financial, and other regulated sectors face rising pressure to review large volumes of complex documents against strict and frequently evolving standards, while preserving the traceability needed to defend decisions to auditors and regulators. Large language model (LLM) based automation offers clear efficiency gains, but single-agent deployments raise well-documented concerns about hallucination, opacity, and insufficient auditability, particularly as regulatory regimes such as the European Union Artificial Intelligence Act begin to impose explicit human oversight and logging obligations on high-risk systems. This paper proposes a conceptual framework for multi-agent AI quality control in the review of regulated documents. The framework decomposes the review task across specialized agents, including extraction, domain compliance checking, adversarial cross-validation, audit and traceability, and human interface functions, coordinated by an orchestrator and grounded in a governed regulatory knowledge base. We describe design principles, architecture, a step-by-step workflow, quality control mechanisms such as confidence scoring and entailment-based cross-checking, and evaluation metrics suited to regulated settings. An illustrative scenario drawn from pharmaceutical Good Practice (GxP) document review demonstrates the framework in use, and a secondary example from financial disclosure review indicates its generality. We close with a discussion of open challenges, including correlated error propagation across agents built on shared foundation models, computational overhead, regulatory acceptance, and security risks such as prompt injection, and we outline directions for empirical validation.