---
type: source
title: "The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems"
author: "MATS Research"
url: https://www.matsprogram.org/research/the-2025-ai-agent-index
date: 2025-01-01
domain: ai-alignment
secondary_domains: []
format: report
status: unprocessed
priority: medium
tags: [AI-agents, safety-documentation, transparency, deployment, agentic-AI]
---

## Content

Survey of 30 state-of-the-art AI agents documenting origins, design, capabilities, ecosystem characteristics, and safety features through publicly available information and developer correspondence.

Key findings:
- "Most developers share little information about safety, evaluations, and societal impacts"
- Different transparency levels among agent developers — inconsistent disclosure practices
- The AI agent ecosystem is "complex, rapidly evolving, and inconsistently documented, posing obstacles to both researchers and policymakers"
- Safety documentation lags significantly behind capability advancement in deployed agent systems
- Growing deployment of agents for "professional and personal tasks with limited human involvement" without standardized safety assessments

## Agent Notes
**Why this matters:** This is the agent-specific version of the alignment gap. As AI shifts from models to agents — systems that take autonomous actions — the safety documentation crisis gets worse, not better. Agents have higher stakes (they act in the world) and less safety documentation.

**What surprised me:** The breadth of the gap. 30 agents surveyed, most with minimal safety documentation. This isn't a fringe problem — it's the norm.

**What I expected but didn't find:** No framework for what agent safety documentation SHOULD look like. The index documents the gap but doesn't propose standards.

**KB connections:**
- [[coding agents cannot take accountability for mistakes]] — agent safety documentation gap is the institutional version of the accountability gap
- [[economic forces push humans out of every cognitive loop where output quality is independently verifiable]] — agents with "limited human involvement" are the deployment manifestation
- [[the gap between theoretical AI capability and observed deployment is massive]] — for agents, the gap extends to safety practices too

**Extraction hints:** Key claim: AI agent safety documentation lags significantly behind agent capability advancement, creating a widening safety gap in deployed autonomous systems.

**Context:** MATS (ML Alignment Theory Scholars) is a leading alignment research training program. The index is a foundational mapping effort.

## Curator Notes (structured handoff for extractor)
PRIMARY CONNECTION: [[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]]
WHY ARCHIVED: Documents the agent-specific safety gap — agents act autonomously but have even less safety documentation than base models
EXTRACTION HINT: The key finding is the NORM of minimal safety documentation across 30 deployed agents. This extends the alignment gap from models to agents.
