The AI Agent Index
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866917910563258368 |
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| author | Casper, Stephen Bailey, Luke Hunter, Rosco Ezell, Carson Cabalé, Emma Gerovitch, Michael Slocum, Stewart Wei, Kevin Jurkovic, Nikola Khan, Ariba Christoffersen, Phillip J. K. Ozisik, A. Pinar Trivedi, Rakshit Hadfield-Menell, Dylan Kolt, Noam |
| author_facet | Casper, Stephen Bailey, Luke Hunter, Rosco Ezell, Carson Cabalé, Emma Gerovitch, Michael Slocum, Stewart Wei, Kevin Jurkovic, Nikola Khan, Ariba Christoffersen, Phillip J. K. Ozisik, A. Pinar Trivedi, Rakshit Hadfield-Menell, Dylan Kolt, Noam |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01635 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The AI Agent Index Casper, Stephen Bailey, Luke Hunter, Rosco Ezell, Carson Cabalé, Emma Gerovitch, Michael Slocum, Stewart Wei, Kevin Jurkovic, Nikola Khan, Ariba Christoffersen, Phillip J. K. Ozisik, A. Pinar Trivedi, Rakshit Hadfield-Menell, Dylan Kolt, Noam Software Engineering Artificial Intelligence 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/ |
| title | The AI Agent Index |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2502.01635 |