AI Economist Agent: An Agentic Framework for Model-Grounded Economic Analysis with RAG, Knowledge Graphs, and Large Language Models

The article presents an AI economist agent framework that integrates Retrieval-Augmented Generation (RAG) with knowledge graphs and large language models (LLMs) for economic scenario analysis. This model leverages economic data and theory to enhance the coherence and traceability of generated narratives, as demonstrated in applications related to U.S. inflation persistence and bank stress-test narratives. This framework is significant for practitioners as it allows for more rigorous and data-grounded economic analysis, improving the reliability of insights derived from LLMs in economic contexts.