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

2026-02-19

AI Summary

This study addresses the fragmented and rapidly evolving landscape of AI agents, which lacks standardized documentation and thereby impedes effective tracking of technical advances and safety characteristics. To bridge this gap, the work presents the first comprehensive index of AI agents, systematically cataloging the provenance, design, capabilities, and safety disclosures of 30 state-of-the-art systems. Through an integrated methodology combining literature review, public data collection, and direct engagement with developers, the research conducts a structured comparative analysis. Findings reveal that most developers provide insufficient transparency regarding safety evaluations and societal impacts. The resulting publicly accessible online index establishes a transparent and comparable benchmark to support academic inquiry and regulatory oversight, significantly advancing the transparency of the AI agent ecosystem.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Data transparency and provenance

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

Problem

• Agentic AI systems
• AI agent ecosystem
• transparency
• safety features
• documentation

Innovation

• AI Agent Index
• agentic AI systems
• safety features
• transparency
• systematic benchmarking