An agentic AI assistant for country-level economic modeling: Methods, data, and expert evaluation

The demand for high-quality, rapid economic analysis to navigate complex issues faced by many low- and middle-income countries has led to the development of detailed structural simulation models, such as Computable General Equilibrium (CGE) models. Policy analysis with such models requires deep knowledge of their structure and applicability to the policy issues at hand. Policymakers in these settings often lack access to the expertise required for articulating, analyzing, and interpreting the relevant causal chains captured by the models. Attempting to circumvent these barriers by submitting complex economic questions directly to off-the-shelf large language models (LLMs) introduces severe analytical risks, including hallucinations and insufficient expert guidance. To resolve this limitation, we developed and empirically evaluated an agentic AI assistant called RIAPA-AI that integrates LLMs with a CGE model. We evaluated the performance of RIAPA-AI against expert human CGE modelers and a general-purpose plain LLM baseline across samples of complex economic scenarios, utilizing an independent panel of senior economists to grade the outputs. Our statistical analysis reveals no statistically significant difference in analytical accuracy between RIAPA-AI and human experts, while the AI accelerates reproducible policy analysis from weeks to minutes. Furthermore, by operating without manual processing limits, RIAPA-AI eliminates the 6.7% error rate observed among human modelers. Conversely, the general-purpose plain LLM exhibits profound failure rates, failing to achieve policy-ready scores in over 60% of depth evaluations. Without an underlying CGE model acting as a bounding force to reflect economic structural constraints, the general-purpose plain LLM defaults to linear economic assumptions and inserts unmodeled socio-political narratives. Crucially, by explicitly restricting the AI's narrative interpretation solely to deterministic numerical outputs, RIAPA-AI mitigates the risk of unverified assumptions and logic hallucinations. We conclude that by deploying an agentic AI assistant that layers a generative AI over a formal CGE model, RIAPA-AI successfully delivers sensible, rigorous, and rapid policy analysis.