A multi-agent RAG system for generating SCORM courses from enterprise documents

Corporate onboarding requires the effective transfer of complex organizational knowledge embedded in internal policies and procedural documents; however, existing artificial intelligence (AI)-driven course generation systems primarily target academic or public knowledge domains. This gap limits the scalability and consistency of enterprise training, particularly in regulated environments where factual accuracy is critical. In this study, we present a multi-agent pipeline that automatically generates Sharable Content Object Reference Model (SCORM) 1.2-compliant e-learning courses from heterogeneous enterprise documents using large language models and retrieval-augmented generation (RAG). The system integrates four stages: semantic document ingestion with structure-aware chunking and embedding, an autonomous ReAct-based architect agent for course design, a parallel content generation pipeline combining multi-query retrieval and neural reranking, and standards-compliant SCORM packaging for deployment in learning management systems. Evaluated using real-world occupational safety documents, the system produced a complete multi-module course with structured lessons and assessments within minutes, demonstrating end-to-end automation of instructional design grounded exclusively in source materials. By ensuring traceability of generated content to organizational knowledge, the approach reduces the risk of hallucinations.