The AI Agent Index

2025-02-03

AI Summary

A publicly accessible, structured database of agentic AI systems is currently lacking, hindering systematic evaluation of their capabilities and safety risks. Method: This work introduces the first open-source, agent-centric database, constructed through collaborative engagement with developers and analysis of publicly available documentation. It employs a hybrid approach combining manual curation with semi-automated information extraction, underpinned by a standardized indexing framework that structures and cross-aligns technical components, application domains, and safety practices. Contribution/Results: The database initially catalogs数十 mainstream AI agents, revealing—for the first time—a pervasive industry-wide asymmetry: comprehensive capability disclosure contrasted with severe underreporting of safety practices. By establishing transparency and enabling rigorous, cross-system assessment, this resource fills a critical gap in agentic AI governance, providing foundational infrastructure for safety auditing, technical accountability, and responsible innovation.

Technology Category

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

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

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/

Problem

• AI Agents
• Database Deficiency
• Security Measures

Innovation

• AI Agent Index
• Risk Management
• Transparency in AI Safety