Commentary: Qi, J. et al. Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security. Acad. AI Appl. 2026, 2, 8260 Anastasios Stavropoulos1,2,*

https://www.academia.edu/3071-0286/2/3/10.20935/AcadAI8435 This survey offers a rigorous and welcome map of agentic artificial intelligence (AI) trustworthiness, organised around two dimensions the authors identify as critical for high-risk deployment: safety and robustness, and privacy and system security. By design, the survey treats value alignment, transparency, fairness, and accountability as relevant contexts rather than as core dimensions. This commentary advances a friendly amendment: accountability is not a peripheral dimension that can be deferred, but the binding constraint on agentic-AI trustworthiness. The reason it resists the survey’s stage-targeted, technical treatment is structural and temporal. Accountability mechanisms are deliberative and operate at human, institutional speed; agentic systems act autonomously, continuously, and at scale. This asymmetry—a governance lag—means that even a fully implemented suite of technical mitigations leaves a residual gap that only governance can close, while prevailing governance instruments remain calibrated to human-paced oversight. A complete trustworthiness agenda must therefore foreground accountability as a first-order design and governance problem. https://www.academia.edu/journals/academia-ai-and-applications/articles?source=journal-top-nav