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Defining AI Agents: A Compendium of Criteria, Metrics, and BenchmarksDefining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks

📅 2026-09-12 ⏱️ 约 5 分钟阅读⏱️ 5 min read ✍️ AI导航编辑部✍️ AI Nav Editorial 🔗 arxiv.org
Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks
📝 内容摘要📝 Summary

Computer Science > Artificial Intelligence [Submitted on 10 Sep 2026] Title:Defining AI Agents: A Compendium of CrComputer Science > Artificial Intelligence [Submitted on 10 Sep 2026] Title:Defining AI Agents: A Compendium of Cr

📌 核心要点

  • Computer Science > Artificial IntelligenceComputer Science > Artificial Intelligence
  • [Submitted on 10 Sep 2026][Submitted on 10 Sep 2026]
  • Title:Defining AI Agents: A Compendium of Criteria, Metrics, and BenchmarksTitle:Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks

Computer Science > Artificial Intelligence

[Submitted on 10 Sep 2026]

Title:Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks

View PDF HTML (experimental)Abstract:The term agent in artificial intelligence lacks a standard definition, complicating the evaluation, comparison, and reproducibility of AI agent research. We address this ambiguity through a survey organized around five dimensions of agenticness: environmental interaction, learning and adaptation, autonomy, goal-directed behavior, and temporal coherence. For each dimension, we examine how the underlying capability has been conceptualized across prior work and synthesize the metrics, benchmarks, and evaluation frameworks used to assess it. This review provides a structured account of the current landscape of agent evaluation, highlighting both established approaches and areas where evaluation remains limited or inconsistent. We additionally introduce the Agent Compendium, a public-facing digital resource that organizes and extends the evaluation methods identified through this review. Together, the survey and compendium provide a common structure for evaluating and comparing agent capabilities across AI systems, supporting more reproducible research, clearer communication, and more systematic study of artificial agents.

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Computer Science > Artificial Intelligence

[Submitted on 10 Sep 2026]

Title:Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks

View PDF HTML (experimental)Abstract:The term agent in artificial intelligence lacks a standard definition, complicating the evaluation, comparison, and reproducibility of AI agent research. We address this ambiguity through a survey organized around five dimensions of agenticness: environmental interaction, learning and adaptation, autonomy, goal-directed behavior, and temporal coherence. For each dimension, we examine how the underlying capability has been conceptualized across prior work and synthesize the metrics, benchmarks, and evaluation frameworks used to assess it. This review provides a structured account of the current landscape of agent evaluation, highlighting both established approaches and areas where evaluation remains limited or inconsistent. We additionally introduce the Agent Compendium, a public-facing digital resource that organizes and extends the evaluation methods identified through this review. Together, the survey and compendium provide a common structure for evaluating and comparing agent capabilities across AI systems, supporting more reproducible research, clearer communication, and more systematic study of artificial agents.

References & Citations

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Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)

Connected Papers (What is Connected Papers?)

Litmaps (What is Litmaps?)

scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub (What is DagsHub?)

Gotit.pub (What is GotitPub?)

Hugging Face (What is Huggingface?)

ScienceCast (What is ScienceCast?)

Demos

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Influence Flower (What are Influence Flowers?)

CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

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来源arxiv.org· 本文为编辑整理,仅供参考