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

TEXT START: We propose an AI economist agent for economic and financial scenario analysis.

The Dissection

This is a control and legitimacy layer for cognitive automation. RAG retrieves evidence, the knowledge graph formalizes mechanisms, registered models perform calculations, tests gate outputs, and the LLM assembles the report. It converts an economist’s workflow into a modular, auditable production pipeline. The real product is not a new economic theory. It is a deployable system for manufacturing scenario analysis.

The Core Fallacy

Its central blind spot is confusing reliability with continued human necessity. The framework addresses hallucination, traceability, and reproducibility; it does not address P1–P3. If it works, it makes economists less necessary by decomposing their labor into retrieval, mechanism mapping, calculation, validation, and reporting. Registered models and predefined tests are not human moats. They are rails that let machines enter institutional workflows at scale.

Hidden Assumptions

• Predefined tests can identify usable results even when unprecedented shocks invalidate the model structure.
• Retrieval systems and knowledge graphs preserve causal mechanisms rather than flattening context, bias, and source selection into machine-readable artifacts.
• Existing quantitative models remain adequate under distribution shift, reflexive market behavior, and nonlinear crises.
• An auditable chain of sources and calculations is equivalent to truth or decision quality. It is not.
• Human economists remain necessary for judgment, liability, and sign-off once the same functions can be standardized and automated.
• Institutions will use the framework cooperatively, rather than competing to reduce analyst headcount and concentrate proprietary models, data, and compute.
• Scenario analysis remains an endpoint instead of becoming a recursive machine process that generates, tests, revises, and deploys its own analysis.

Social Function

Transition management with a genuine partial-truth core, wrapped in prestige signaling. The paper offers a real answer to the problem of making LLM-generated analysis auditable enough for institutional use. Its social function is to domesticate automation for banks, policymakers, and analysts while preserving the appearance that the economist remains the central productive actor. It is not simple copium. It is a bridge from human-led analysis to machine-led analysis.

The Verdict

This paper is the paperwork phase of economist obsolescence. Its safeguards may reduce errors and delay institutional resistance, but they do not preserve mass analytical employment. Under the Discontinuity Thesis, the framework advances Cognitive Automation Dominance: it turns economic expertise into documented components, registered models, and testable handoffs. The agent is not rescuing the economist. It is packaging the economist’s job for industrial replacement.