Back to AI Research

AI Research

GUIDE: Governed Unified Intelligence for Document-t... | AI Research

Key Takeaways

  • GUIDE is a governed multi-agent framework designed to automate the conversion of complex, multimodal enterprise guideline documents into structured, executab...
  • Enterprise guideline documents are heterogeneous and multimodal, combining narrative text, complex tables, and embedded images.
  • Existing LLM and VLM systems face hallucinated content, table structure degradation, and lack governed workflows extending beyond extraction to validation and artifact generation.
  • This leaves enterprises to perform this manually, consuming 2-3 days per document.
  • To address this, we introduce GUIDE, a governed multi-agent framework built on a shared versioned rule store with schema-validated inter-agent contracts and end-to-end provenance tracking.
Paper AbstractExpand

Enterprise guideline documents are heterogeneous and multimodal, combining narrative text, complex tables, and embedded images. Existing LLM and VLM systems face hallucinated content, table structure degradation, and lack governed workflows extending beyond extraction to validation and artifact generation. This leaves enterprises to perform this manually, consuming 2-3 days per document. To address this, we introduce GUIDE, a governed multi-agent framework built on a shared versioned rule store with schema-validated inter-agent contracts and end-to-end provenance tracking. Six specialized agents handle parsing, VLM-driven extraction, consistency checking, evaluation, human-in-the-loop (HITL) escalation, and persona-tailored artifact synthesis. Evaluated on 120 real-world enterprise guideline documents, GUIDE achieves 96% document success, extracts 3,896 rules with 71.4% auto-approved, produces 812 deployment-ready artifacts, and reduces turnaround to 40-125 minutes per document.

GUIDE is a governed multi-agent framework designed to automate the conversion of complex, multimodal enterprise guideline documents into structured, executable work artifacts. By replacing manual processes that typically take 2–3 days per document, the system reduces turnaround time to 40–125 minutes while ensuring data consistency and traceability.

The Problem: Manual Bottlenecks

Enterprise guidelines often contain a mix of narrative text, complex tables, and images. Current automated systems struggle with these documents because they frequently produce hallucinations, fail to interpret table structures, and lack a governed workflow to validate outputs. Consequently, project and quality managers must manually read, interpret, and convert these documents into instructions and statements of work, creating a significant operational bottleneck.

How GUIDE Works

The framework treats document-to-artifact conversion as a data management challenge, utilizing a central, versioned rule store that acts as a shared data layer. Six specialized agents coordinate through this store using schema-validated contracts:

  • Parsing Agent: Separates deterministic text extraction from VLM-based visual processing.

  • Rule Extraction Agent: Identifies candidate rules and normalizes them into a 26-field schema.

  • Consistency Module: Uses embedding-based similarity and natural language inference to deduplicate and align rules.

  • Evaluation Module: Performs two-stage validation—L1 (structural) and L2 (semantic)—to score objects and determine if they require human review.

  • HITL Controller: Manages a dependency-aware human-in-the-loop workflow, routing only flagged items to experts.

  • Artifact Generation Agent: Transforms approved rules into deployment-ready documents, such as annotator guidelines and QA rubrics.

Performance Results

In an evaluation using 120 real-world enterprise documents, the researchers reported that GUIDE achieved a 96% document success rate. The system extracted 3,896 rules, with 71.4% of them auto-approved by the semantic evaluation engine. Additionally, the framework produced 812 deployment-ready artifacts. Compared to a monolithic baseline that lacked the governed rule store and validation layers, GUIDE significantly reduced hallucination rates (from 15.7% to 3.2%) and contradictions (from 7.8% to 2.9%).

Limitations and Considerations

The researchers note that the system’s performance is sensitive to the quality of the input documents; VLM extraction stability decreases when processing low-quality scans or complex, borderless tables. Furthermore, the framework currently struggles with adapting technical rules for non-expert audiences, and its calibration mechanism requires accumulating zero-edit human approvals over time, meaning early deployment cycles may have limited scoring stability. Future work is intended to address these challenges, including expanding the system to support multilingual settings and broader regulatory or clinical domains.

Comments (0)

No comments yet

Be the first to share your thoughts!