The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems

TL;DR

The 2025 AI Agent Index documents information regarding the origins, design, capabilities, ecosystem, and safety features of 30 state-of-the-art AI agents based on publicly available information and email correspondence with developers.

Abstract

Agentic AI systems are increasingly capable of performing professional and personal tasks with limited human involvement. However, tracking these developments is difficult because the AI agent ecosystem is complex, rapidly evolving, and inconsistently documented, posing obstacles to both researchers and policymakers. To address these challenges, this paper presents the 2025 AI Agent Index. The Index documents information regarding the origins, design, capabilities, ecosystem, and safety features of 30 state-of-the-art AI agents based on publicly available information and email correspondence with developers. In addition to documenting information about individual agents, the Index illuminates broader trends in the development of agents, their capabilities, and the level of transparency of developers. Notably, we find different transparency levels among agent developers and observe that most developers share little information about safety, evaluations, and societal impacts. The 2025 AI Agent Index is available online at https://aiagentindex.mit.edu

Figures (19)

• Figure 1: Interest in AI agents is growing. 2025 has seen a sharp increase in interest in AI agents. This is reflected in an increase of new Google search terms related to agentic AI products (blue bars) as well as Google Scholar paper counts for "AI agent" or "agentic AI" (red line). Accumulation of individual releases of agentic AI products included in this Index is shown by category: 1pt1.5ptchat_yellowchats with agentic tools, 1pt1.5ptenterprise_redenterprise agents, and 1pt1.5ptbrowser_bluebrowser agents. See \ref{['fig:agent_release_timeline']} for details on releases and \ref{['app:search']} for details on public interest.
• Figure 2: Inclusion criteria for Index. Candidate agents flow through three criteria categories from left to right. Systems must satisfy all agency criteria, at least one impact criterion, and all practicality criteria. See \ref{['sec:inclusion_criteria']} for details of each criterion.
• Figure 3: For 198 out of 1350 fields, we were unable to find any information (gray). This is most common in the "Ecosystem Interaction" and "Safety, Evaluation, and Impact" categories. Non-empty information fields are 14 words long on average.
• Figure 4: Comparison between Chinese, US, and other agent developers. To mitigate potential blind spots, a Chinese AI ecosystem expert reviewed our coverage of safety frameworks, including those published only in Mandarin.
• Figure 5: Certain levels of autonomy are more common (shown as wider) depending on the category. 1pt1.5ptbrowser_blueBrowser-based and 1pt1.5ptenterprise_reddeployed enterprise agents are the most agentic. The resulting agents deployed through enterprise designers are significantly more agentic than the process of designing the agents.