The AI Agent Index

TL;DR

Problem: public understanding and governance of deployed agentic AI systems are hampered by fragmented, incomplete safety documentation. Approach: the authors construct the AI Agent Index, a public database documenting 67 deployed agentic systems with 33-field agent cards capturing components, uses, and safety practices, using publicly available information and developer correspondence. Contributions: (i) a structured documentation framework for agentic AI, (ii) empirical characterization of deployment patterns, openness, and risk-management transparency, (iii) a discussion of governance implications and future documentation directions. Significance: provides a baseline for auditing, policymaking, and risk assessment and highlights critical gaps in safety reporting.

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/

Figures (7)

• Figure 1: Most AI agent developers in the index provide some public documentation (70.1%), while about half (49.3%) release their underlying code.
• Figure 2: Only 19.4% of indexed agentic systems disclose a formal safety policy, and fewer than 10% report external safety evaluations.
• Figure 3: Decision graph for determining inclusion in the index: We focused on indexing agentic systems (as opposed to models or development frameworks) and drew on the four characteristics of agency from chan2023harms: underspecification, directness of impact, goal-directedness, and long-term planning. In total, we indexed 67 systems.
• Figure 4: Agentic systems are being deployed at a steadily increasing rate.
• Figure 5: Most agentic systems are created by developers in the USA. In this figure, some developers' countries are counted multiple times due to producing multiple indexed systems. Google DeepMind is counted 3x, while OpenAI, National University of Singapore, UC Berkeley, and Stanford University are each counted 2x.