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

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.