The AI Agent Index

• URL: http://arxiv.org/abs/2502.01635v1
• Date: Mon, 03 Feb 2025 18:59:13 GMT
• Title: The AI Agent Index
• Authors: Stephen Casper, Luke Bailey, Rosco Hunter, Carson Ezell, Emma Cabalé, Michael Gerovitch, Stewart Slocum, Kevin Wei, Nikola Jurkovic, Ariba Khan, Phillip J. K. Christoffersen, A. Pinar Ozisik, Rakshit Trivedi, Dylan Hadfield-Menell, Noam Kolt,
• Abstract summary: Agentic AI systems can plan and execute complex tasks with limited human involvement. There is currently no structured framework for documenting the technical components, intended uses, and safety features of agentic systems. The AI Agent Index is the first public database to document information about currently deployed agentic AI systems.
• Score: 8.48525754659057
• License:
• Abstract: 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 at https://aiagentindex.mit.edu/

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