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The AI Agent Index

T0 review · 0 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read The first public index of 67 agentic AI systems shows safety disclosure lags far behind capability information.

desk verdict A genuinely first-of-its-kind public registry of deployed agentic systems, with an honest limitations section and a robust headline finding about sparse safety disclosure. read the letter →

arxiv 2502.01635 v1 pith:IB2L3BLN submitted 2025-02-03 cs.SE cs.AI

Stephen Casper , Luke Bailey , Rosco Hunter , Carson Ezell , Emma Cabalé , Michael Gerovitch , Stewart Slocum , Kevin Wei
show 7 more authors
Nikola Jurkovic Ariba Khan Phillip J.K. Christoffersen A. Pinar Ozisik Rakshit Trivedi Dylan Hadfield-Menell Noam Kolt
This is my paper · ORCID
classification cs.SEcs.AI
keywords agenticAIAgentIndextransparencysafetydisclosureriskmanagementcardsgovernancepublicdatabase
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces the AI Agent Index, a public database of 67 currently deployed agentic AI systems that can plan and act with limited human involvement. For each system it documents 33 fields across six categories, from backend models and tool use to guardrails, safety evaluations, and developer information, drawing on public sources and developer feedback. The central finding is an asymmetry: developers publish substantial information about what their agents can do, but almost none about how they are managed for safety. The authors argue this is the first structured, cross-system evidence of that gap, and that it gives policymakers and auditors a concrete starting point for transparency and governance efforts.

What carries the argument

The machinery is the inclusion decision graph plus the agent card template. The decision graph starts from a named, 'agentic' system and requires it to accomplish a diverse range of tasks with a meaningfully higher degree of agency than ChatGPT-4o, judged using the four characteristics of agency the paper adopts from its background review: underspecification, directness of impact, goal-directedness, and long-term planning. It excludes plain language models, development frameworks without a qualifying flagship system, and systems that cannot be used off the shelf; the final node lets the authors include important announced-but-not-yet-deployed systems at their discretion. Applied to each included system, the 33-field agent card standardizes what is recorded, and its use of 'None' or 'Unknown' is the mechanism that surfaces the safety-transparency gap.

What would settle it

Re-run the inclusion graph with the agency threshold anchored to a clearly weaker baseline, such as GPT-3.5, or drop the discretionary final node, and recompute the share of indexed systems disclosing a formal safety policy; if that share moves by more than a few percentage points, the reported 19.4% is an artifact of the chosen threshold rather than a property of the ecosystem. Alternatively, check the public documentation of the 43 developers who never replied to the index team; if a substantial fraction of them publish formal safety policies, the 'limited information' conclusion partly reflects non-response.

Watch

Extended reading notes

Core claim

The paper's central discovery is the transparency asymmetry documented by the index: while 70.1% of the 67 indexed systems publicly release documentation and 49.3% release code, only 19.4% disclose a formal safety policy, 7.5% report external safety testing, and 9% report public safety evaluations by the developer. The 33-field agent cards record 'None' or 'Unknown' when information is absent, which is what turns the asymmetry into a measurable, citable finding. The paper frames this as the first public database of deployed agentic systems and the first structured evidence that the agentic AI ecosystem is transparent about capabilities and applications but opaque about safety and risk management.

Load-bearing premise

The entire sample rests on the authors' judgment that a system has 'a meaningfully higher degree of agency than ChatGPT-4o,' plus a discretionary final inclusion step, so the 67-system list and every percentage derived from it depend on that subjective threshold.

Editorial extensions

If this is right

  • The index gives policymakers a first evidence base: the deployment rate, geographic and institutional spread, and domain concentration of agentic systems are now documented rather than anecdotal.
  • Governance attention should focus on corporate developers, US-based organizations, and software-engineering and computer-use agents, which together dominate the index.
  • The transparency gap argues for disclosure mechanisms as an early intervention, including structured bug bounties, coordinated external testing of agents, and integration of indices into model registries.
  • Future documentation efforts should adopt the paper's method of explicit 'None' and 'Unknown' recording and should scope their selection criteria to reduce the subjectivity the authors acknowledge.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The index likely understates true safety-practice disclosure: 64% of developers did not respond, and internal or unpublished safety processes are invisible by construction, so the headline percentages may be a lower bound on practice but an upper bound on public transparency.
  • The ChatGPT-4o anchor for agency will date quickly as frontier models become more agentic, making the December 31, 2024 snapshot hard to compare with later indices unless the threshold is re-anchored.
  • The 9% and 7.5% figures are small enough that a re-sampling with a slightly different inclusion rule could shift them materially; the paper's qualitative conclusion of a safety gap is more robust than its exact percentages.
  • A natural extension the paper does not build is a per-system transparency score separating capability disclosure from safety disclosure, letting users and regulators track whether the gap widens or closes over time.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

0 major / 6 minor

Summary. This paper introduces the AI Agent Index, a structured public database of 67 deployed agentic AI systems as of December 31, 2024. The authors develop inclusion criteria based on the four agency characteristics from Chan et al. (2023) (underspecification, directness of impact, goal-directedness, and long-term planning) plus an explicit decision graph, and they populate 33 fields per system from public sources and developer correspondence, with a reported 36% developer response rate. The main empirical finding is an asymmetry in public documentation: 70.1% of indexed systems have public documentation and 49.3% release code, while only 19.4% disclose a formal safety policy, 7.5% report external safety testing, and 9% report public safety evaluations by the developer. The paper also reports distributions across countries, developer types, and application domains, and it closes with governance recommendations. Limitations, including English-language bias, public-documentation bias, incomplete developer verification, and the subjectivity of the inclusion threshold, are acknowledged in Section 6.

Significance. If the index is accepted as representative, this is the first structured, system-level empirical evidence of a documentation gap between capability and usage information on the one hand and safety and risk-management information on the other hand for agentic AI. The contribution is timely and useful for users, auditors, researchers, and policymakers. The paper's strengths include the release of raw data and archived citations, a detailed sample agent card, an explicit decision graph for inclusion, and the decision not to use the index as a scorecard in order to reduce gaming incentives. The acknowledged subjectivity of the inclusion criteria and the reliance on public documentation limit precision, but the qualitative finding is robust: even a generous reclassification of marginal systems would leave safety disclosure far below capability disclosure, and the selection bias toward publicly documented systems makes the safety deficit conservative. The paper is therefore credible as a first empirical mapping of the field.

minor comments (6)
  1. [Section 3 / Figure 3] The final discretionary node of the inclusion decision graph is not constrained by the system being deployed or open source, and the examples given (OpenAI o3, Project Mariner) are either not deployed as of the cutoff or are models rather than agentic systems; this sits awkwardly with the stated exclusions and with the 'currently deployed' framing, so the operational rule and the number of affected entries should be stated explicitly.
  2. [Section 3 / Figure 3] The threshold 'meaningfully higher degree of agency than ChatGPT-4o' is not operationalized beyond the footnote about ChatGPT-4o; a brief calibration example or sensitivity check would make the sample boundary more reproducible, although the Section 6 caveat already mitigates the risk to the main conclusion.
  3. [Section 5 / Figure 2] The paper should state explicitly in the main results that the safety percentages measure public availability of documented practices, not the absence of internal practices; Section 6 makes this point, but it is central enough to the interpretation of the headline numbers to appear alongside the findings.
  4. [Appendix A] The sample card for Magentic One lists the announcement date as November 4, 2023, which appears to be a typo for November 4, 2024, given the system's technical report and the paper's timeline.
  5. [Figure 5] The caption should clarify whether the bars count systems or unique organizations, since it states that some developers contribute multiple systems but the reader cannot tell from the figure alone whether the country totals are system-level counts.
  6. [Section 3 / References] Minor editorial issues: 'V onage' should be 'Vonage', 'Moatlesss' should likely be 'Moatless', and the CORE-Bench reference misspells 'Nadgir' as 'Nagdir'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the safety-transparency finding is a direct measurement of public documentation, not a consequence of the authors' assumptions or prior work.

full rationale

The paper's central claim — that developers provide ample capability/usage information but limited safety/risk-management information — is an empirical tally, not a derived result. The percentages in Figures 1–2 are direct counts from the 67 coded agent cards (e.g., 13/67 ≈ 19.4% for a disclosed formal safety policy; 49.3% releasing code), and the raw data are released for independent checking. No equation is involved, and no fitted parameter is later renamed as a prediction. The inclusion criteria from Chan et al. (2023) and the 'meaningfully higher degree of agency than ChatGPT-4o' threshold in Figure 3 determine the sample, not the measured outcome; the paper explicitly disclaims a definition of 'AI agent' ('we do not weigh in on this debate, advocate a particular definition of AI agent, or propose alternative terminology') and discloses the discretionary final node. Some cited background works (e.g., Kolt 2025; Chan et al. 2024a; Slattery et al. 2024) share authors with this paper, but they are used only to motivate the importance of agent transparency, not to establish the empirical finding. The acknowledged limitations — public-only sampling, English-language bias, 36% developer response rate, and coding subjectivity — would, if anything, make the safety-transparency gap harder to detect, so they do not reveal a circular construction. No load-bearing step reduces to the paper's own inputs.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The AI Agent Index introduces no new physical or conceptual entities; it is a structured database. The central claims rest on domain assumptions about how to define and sample agentic systems and about the evidentiary value of public documentation for safety practices. No free parameters are fitted to data, because the paper makes no quantitative predictions or model fits.

assumptions (3)
  • domain assumption Chan et al. (2023)'s four characteristics (underspecification, directness of impact, goal-directedness, long-term planning) provide a workable characterization of agentic AI systems for inclusion purposes.
    Section 3 (Methods) uses these characteristics in the decision graph to determine whether a system is included in the index. The authors explicitly decline to offer their own definition of 'AI agent' and adopt this characterization from prior work.
  • domain assumption Publicly available information, supplemented by a 36% developer response rate, is a sufficient basis for documenting safety and risk-management practices.
    The index codes fields as 'None' or 'Unknown' when no public information exists (Section 4), and the finding of limited safety disclosure depends on this proxy. Section 6 acknowledges that internal safety documents or practices may exist but were not discoverable from public sources.
  • domain assumption The set of 67 systems identified via web searches, literature review, benchmark leaderboards, and community lists is an informative sample of deployed agentic systems.
    Section 3 describes the identification process. Section 6 concedes the index is not comprehensive, likely overrepresents publicly available and English-language systems, and may miss systems used internally or released after the cutoff date.

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Cite this review

Pith. "Pith review of The AI Agent Index." pith.science (2026). https://pith.science/paper/IB2L3BLN

@misc{pith2026250201635,
  author       = {Pith},
  title        = {Pith review of: The AI Agent Index},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IB2L3BLN}},
  note         = {Machine review of arXiv:2502.01635}
}
read the original 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

Figures reproduced from arXiv: 2502.01635 by the authors.

Figure 1
Figure 1. Most AI agent developers in the index provide some public documentation (70.1%), while about half (49.3%) release their underlying code. 0 25 50 75 100 % With Safety Measure Publicly reported safety testing by developer Publicly reported external safety testing Publicly available formal safety policy 9.0% 7.5% 19.4% [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
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 Chan et al. (2023): underspecification, directness of impact, goal-directedness, and long-term planning. In total, we indexed 67 systems. and different configurations (e.g., “HelpfulAgent-Claude3.5- Sonnet” vs. “HelpfulAgent… view at source ↗
Figure 5
Figure 5. Most agentic systems are created by developers in the USA. In this figure, some developers’ countries are counted mul￾tiple 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. 26.9% 73.1% Developer Types Academic Industry [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figures from the paper (2 more)
Figure 6
Figure 6. Figure 6: , we show the developers of agents broken down based on whether they are projects from academic labs or companies in industry. 18 (26.9%) are academic while 49 (73.1%) are from companies. The majority of indexed systems specialize in software engineering and/or compute…
Figure 7
Figure 7. Figure 7: The majority of indexed systems specialize in software engineering and/or computer use. The majority of indexed systems have released code and/or documentation. Developers are relatively publicly forthcoming about details related to usage and capabilities. In [PITH_FU…

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Forward citations

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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.