Benchmarking Intelligence: A Critical Review of Evaluation Frameworks for AI Agents in Automated scRNA-seq Analysis

Abstract

The rapid emergence of autonomous, multi-step analysis-capable artificial intelligence agents is fundamentally transforming the computational biology research paradigm for single-cell RNA sequencing (scRNA-seq). These agents can autonomously navigate complex analytical pipelines, yet the evaluation frameworks currently employed remain anchored in traditional static benchmarking methodologies. Such approaches predominantly rely on endpoint metrics—including the adjusted Rand index (ARI) and normalized mutual information (NMI)—to assess the output of individual algorithms in isolation. These conventional methods fail to capture the core competencies of AI agents, such as multi-step decision-making capacity, adaptability across heterogeneous data conditions, and analytical stability throughout the workflow. This review systematically examines the datasets and evaluation metrics prevalent in contemporary scRNA-seq benchmarking studies, identifying critical methodological shortcomings in their applicability to AI agent assessment. To address these deficiencies, we propose a process-oriented evaluation framework centered on transparency, computational efficiency, and biological validity. The ScAI Bench protocol introduces a multi-dimensional evaluation system encompassing process-tracking assessment, scenario-based testing with publicly available real-world datasets, and rigorous reproducibility verification. Unlike traditional approaches that focus exclusively on final outputs, this framework prioritizes the quality of agent decision-making throughout the analytical process. Its overarching objective is to establish a unified, community-adoptable evaluation standard that aligns with the sophisticated capabilities of AI agents in single-cell analysis. Furthermore, this review provides a strategic roadmap for advancing autonomous analytical methods within single-cell genomics.