The financial industry has long struggled to bridge the gap between standardized XBRL data and the nuanced reasoning needed for deep analysis. Traditional retrieval methods often treat complex disclosures as flat text, losing critical semantic relationships between line items, footnotes, and temporal periods.

This session introduces Graph-Grounded Agentic Retrieval. Using open-source tools like docling and docling-graph, we transform raw XBRL filings into hierarchical knowledge graphs that mirror the inherent structure of financial reporting. We also offer a data-driven demonstration of why graph-based approaches are superior for AI agents. Finally, as part of the FINOS AI Evaluation and Benchmarking stream, we use a rigorous framework to map the "reasoning trajectory" of agents navigating these graphs. Benchmarking against datasets like FinDER and FinAgentBench, we provide empirical evidence that grounding agents in document structure significantly reduces hallucinations and increases factual consistency over standard RAG.

The audience will leave with a blueprint for a verifiable, high-precision retrieval architecture for regulated financial content.