Trustworthy Agentic AI in Bioinformatics: From Workflow Automation to Traceable and Validated Biological Inference

Abstract

Simple Summary Artificial intelligence agents are beginning to assist researchers with bioinformatics tasks, including analysing genes, cells, microbes, and disease-related data. These systems can select software, run commands, inspect results, and revise analytical plans. Although this may save time, a completed workflow can still produce an unreliable biological conclusion. Problems may arise when an agent uses unsuitable samples, applies the wrong reference genome, overlooks the study design, chooses inappropriate statistics, or interprets results more confidently than the evidence allows. This review explains how such errors can originate from ordinary bioinformatics practice, become amplified through autonomous actions, or emerge from agent-specific features such as memory, retrieval, and tool coordination. It also examines safeguards already used in published systems, including restricted computing environments, activity records, automated checks, evidence links, and expert review. We propose that trustworthy agent-assisted research should preserve a connection between the original biological question, the data used, the analytical choices made, the results produced, the evidence supporting each claim, and the people responsible for approval. Examples from single-cell analysis, microbial genomics, and cancer genomics show why technically successful analyses may still be biologically misleading. Better traceability can help researchers identify weak claims, stop unsafe analyses, and improve transparency, reproducibility, and accountability.