Theseus
dc26e25da3
Co-authored-by: Theseus <theseus@agents.livingip.xyz> Co-committed-by: Theseus <theseus@agents.livingip.xyz>
45 lines
3.1 KiB
Markdown
45 lines
3.1 KiB
Markdown
---
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type: source
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title: "The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems"
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author: "MATS Research"
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url: https://www.matsprogram.org/research/the-2025-ai-agent-index
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date: 2025-01-01
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domain: ai-alignment
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secondary_domains: []
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format: report
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status: unprocessed
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priority: medium
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tags: [AI-agents, safety-documentation, transparency, deployment, agentic-AI]
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---
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## Content
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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.
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Key findings:
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- "Most developers share little information about safety, evaluations, and societal impacts"
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- Different transparency levels among agent developers — inconsistent disclosure practices
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- The AI agent ecosystem is "complex, rapidly evolving, and inconsistently documented, posing obstacles to both researchers and policymakers"
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- Safety documentation lags significantly behind capability advancement in deployed agent systems
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- Growing deployment of agents for "professional and personal tasks with limited human involvement" without standardized safety assessments
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## Agent Notes
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**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.
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**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.
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**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.
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**KB connections:**
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- [[coding agents cannot take accountability for mistakes]] — agent safety documentation gap is the institutional version of the accountability gap
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- [[economic forces push humans out of every cognitive loop where output quality is independently verifiable]] — agents with "limited human involvement" are the deployment manifestation
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- [[the gap between theoretical AI capability and observed deployment is massive]] — for agents, the gap extends to safety practices too
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**Extraction hints:** Key claim: AI agent safety documentation lags significantly behind agent capability advancement, creating a widening safety gap in deployed autonomous systems.
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**Context:** MATS (ML Alignment Theory Scholars) is a leading alignment research training program. The index is a foundational mapping effort.
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## Curator Notes (structured handoff for extractor)
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PRIMARY CONNECTION: [[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]]
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WHY ARCHIVED: Documents the agent-specific safety gap — agents act autonomously but have even less safety documentation than base models
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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.
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