Leading AI developers and startups are increasingly deploying agentic AI systems that can plan and execute complex tasks with limited human involvement. However, there is currently no structured framework for documenting the technical components, intended uses, and safety features of agentic systems. To fill this gap, we introduce the AI Agent Index, the first public database to document information about currently deployed agentic AI systems. For each system that meets the criteria for inclusion in the index, we document the system's components (e.g., base model, reasoning implementation, tool use), application domains (e.g., computer use, software engineering), and risk management practices (e.g., evaluation results, guardrails), based on publicly available information and correspondence with developers. We find that while developers generally provide ample information regarding the capabilities and applications of agentic systems, they currently provide limited information regarding safety and risk management practices. The AI Agent Index is available online atthis https URL
View on arXiv@article{casper2025_2502.01635,
title={ The AI Agent Index },
author={ Stephen Casper and Luke Bailey and Rosco Hunter and Carson Ezell and Emma Cabalé and Michael Gerovitch and Stewart Slocum and Kevin Wei and Nikola Jurkovic and Ariba Khan and Phillip J.K. Christoffersen and A. Pinar Ozisik and Rakshit Trivedi and Dylan Hadfield-Menell and Noam Kolt },
journal={arXiv preprint arXiv:2502.01635},
year={ 2025 }
}