MIT: The AI Agent Index

Summary of Read Full Report

The AI Agent Index is a newly created public database documenting agentic AI systems. These systems, which plan and execute complex tasks with limited human oversight, are increasingly being deployed in various domains.

The index details each system’s technical components, applications, and risk management practices based on public data and developer input. An analysis of the data shows ample information on agentic systems' capabilities and applications. However, the authors found limited transparency regarding safety and risk mitigation.

The authors aim to provide a structured framework for documenting agentic AI systems and improve public awareness. It sheds light on the geographical spread, academic versus industry development, openness, and risk management of agentic systems.

The five most important takeaways from the AI Agent Index, with added details, are:

• The AI Agent Index is a public database designed to document key information about deployed agentic AI systems. It covers the system’s components, application domains, and risk management practices. The index aims to fill a gap by providing a structured framework for documenting the technical, safety, and policy-relevant features of agentic AI systems. The AI Agent Index is available at https://aiagentindex.mit.edu/.
• Agentic AI systems are being deployed at an increasing rate. Systems that meet the inclusion criteria have had initial deployments dating back to early 2023, with approximately half of the indexed systems deployed in the second half of 2024.
• Most indexed systems are developed by companies located in the USA, specializing in software engineering and/or computer use. Out of the 67 agents, 45 were created by developers in the USA. 74.6% of the agents specialize in either software engineering or computer use. While most agentic systems are developed by companies, a significant fraction are developed in academia. Specifically, 18 (26.9%) are academic, while 49 (73.1%) are from companies.
• Developers are relatively forthcoming about details related to usage and capabilities. The majority of indexed systems have released code and/or documentation. Specifically, 49.3% release code, and 70.1% release documentation. Systems developed as academic projects are released with a high degree of openness, with 88.8% releasing code.
• There is limited publicly available information about safety testing and risk management practices. Only 19.4% of indexed agentic systems disclose a formal safety policy, and fewer than 10% report external safety evaluations. Most of the systems that have undergone formal, publicly-reported safety testing are from a small number of large companies.