Evaluating Single-Agent and Multi-Agent AI Architectures for Analytical Modeling

Iterative refinement is central to analytical modeling, where robust outcomes emerge through repeated evaluation, refinement, and validation. As Artificial Intelligence (AI) Agents are increasingly used in analytical workflows, a key architectural question arises: can a single analyst agent manage modeling complexity, or do multi-agent frameworks with explicit role separation justify their higher computational cost?

This paper presents an empirical comparison between (a) a single-agent framework, in which one analyst agent performs data exploration, modeling, evaluation, and revision within a unified workflow, and (b) a multi-agent framework that separates responsibilities across analyst, critic, and refiner agents. We evaluate these architectures across descriptive and predictive analytical tasks.

For well-specified descriptive analytics tasks such as summary statistics, deterministic transformations, the single-agent framework achieves analytical quality on par to multi-agent designs. Single-agent framework requires fewer model invocations and lower token usage, making multi-agent coordination overhead unnecessary. In contrast, for analytical tasks that often involve ambiguity, competing objectives, and subtle modeling failure modes such as data leakage, spurious correlations, and non-linear dependencies, we show that multi-agent workflows more reliably detect these issues, reduce false confidence, and improve generalization at increased computational cost.

We interpret these findings through an analogy to established human and organizational practices, where independent critique complements core implementation to improve robustness. Overall, our results suggest that multi-agent architectures primarily enhance reliability under uncertainty rather than routine analytical accuracy. We conclude that single-agent workflows are preferable for low-risk, well-defined tasks, while multi-agent designs are justified for high-stakes modeling tasks where systematic error detection is critical.

AI Agents

Artificial Intelligence

Analytics Workflow

Single Agent vs Multi Agent Architecture

Analytics Modeling Automation 

AI and LLM Applications

Symposium on Data Science and Statistics (SDSS) 2026