How Kensho built a multi-agent framework with LangGraph to solve trusted financial data retrieval

Kensho, S&P Global's AI innovation hub, addresses the challenge of efficiently navigating the company's vast and structured data estate by developing Grounding, a multi-agent framework that unifies data retrieval across various business units. Grounding serves as a centralized access layer for AI applications, ensuring insights are derived from verified datasets and enabling natural language queries against S&P Global's financial data. This system simplifies data access for financial professionals, eliminating the need for navigating complex schemas or specialized query languages, and provides real-time, citation-backed responses. The architecture leverages LangGraph to intelligently route queries to specialized Data Retrieval Agents (DRAs) across domains like equity research and macroeconomics, facilitating coherent insights by aggregating distributed responses. Kensho's custom DRA protocol ensures consistent data access patterns and accelerates collaboration across its multi-agent ecosystem, allowing rapid deployment of specialized financial AI products. The development process highlighted best practices in observability, multi-stage evaluation, and protocol optimization, which enhance system efficiency and maintain the reliability required in financial services.