A Neurosymbolic Multi-Agent System for Explainable and Traceable Decision-Making in Organizational Contexts

The increasing use of Generative Artificial Intelligence (AI) in Human Resource Management (HRM) raises critical challenges for transparency, explainability, and accountability. While Large Language Models (LLMs) offer powerful natural-language interfaces, their tendency to hallucinate and their non-transparent reasoning limit their suitability for decision-making in HR contexts. We propose a transparent-by-design neurosymbolic decision-support system that integrates a provenance-aware knowledge graph with a modular multi-agent architecture and a constrained LLM interface. A scenario-based evaluation on a public HR dataset using a set of operational, strategic, and evidence-retrieval HR analytics queries shows that the system achieves mean fact-level performance ofF1= 0.677, mean traceability completeness of 0.680, hallucination resistance of 0.733, and mean end-to-end latency of 0.067 seconds, alongside a query-level accuracy of 0.733. The paper contributes to Information Retrieval and Generative AI research by illustrating how neurosymbolic grounding, provenance-aware retrieval, and agent-based architectures can enable trustworthy and governable information systems.