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Source note3 min read

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments

Source

arXiv

arxiv.org/abs/2607.05174v1

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Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang
  • Venue: arXiv: cs.AI
  • Published: 2026-07-06
  • Collected: 2026-07-07
  • URL: http://arxiv.org/abs/2607.05174v1
  • PDF: https://arxiv.org/pdf/2607.05174v1
  • DOI: UNKNOWN

Plain-Language Brief

The paper presents AgentGym2, a new evaluation framework for language models (LLMs) that are increasingly being used in real-world production environments. Most existing benchmarks evaluate these models in simplified, idealized settings, but AgentGym2 focuses on real-world scenarios with uncertainties and noises. It measures the ability of agents to execute end-to-end procedures, discover tools through exploration, compose tools for unseen tasks, and remain robust to noisy and underspecified information. The experiments on 15 models, including proprietary and open-source ones like Gemini and GPT-5, show that even state-of-the-art systems face substantial challenges in these realistic environments.

What the Document Says

  • AgentGym2 is a new evaluation framework for language model agents.
  • It is designed to evaluate the performance of agents in real-world end-to-end working demands.
  • The framework measures the ability of agents to execute end-to-end procedures, discover tools via exploration, compose tools for unseen tasks, and remain robust to noisy and underspecified information.
  • 15 proprietary and open-source models were tested on AgentGym2, including Gemini and GPT-5.

Key Concepts Mentioned

  • Language model agents
  • Real-world environments
  • Evaluation framework
  • End-to-end procedures
  • Tool discovery
  • Tool composition
  • Noisy and underspecified information
  • Gemini
  • GPT-5

Claims Made by the Document

  • The need for rigorous and realistic evaluations of language model agents is urgent.
  • Most existing benchmarks evaluate agents in simplified, idealized settings.
  • AgentGym2 addresses this gap by focusing on real-world scenarios with uncertainties and noises.
  • Current state-of-the-art systems like Gemini and GPT-5 struggle on AgentGym2.

Evidence Used

  • The text states that language model agents are being rapidly deployed in production environments, implying the need for rigorous evaluations.
  • The text describes the limitations of existing benchmarks and how AgentGym2 aims to address them.
  • The text provides details about what AgentGym2 measures and the experiments conducted on various models.

Methods / Approach

  • Description of a new evaluation framework (AgentGym2) and its measures.
  • Experiments on 15 proprietary and open-source models.

Limitations Stated by the Authors

  • The paper does not provide specific details about the methods used to create the tasks or the evaluation process itself.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The agent's ability to interpret the implications of the findings and their potential impact on the field is limited due to its role as a summarizer.

Open Questions

  • What specific methods were used to create the tasks in AgentGym2?
  • How were the experiments designed and conducted to measure the performance of the language model agents?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper does not provide details about the specific tasks or how they were created. It also does not discuss the evaluation process in detail.
  • The experiments were conducted on 15 proprietary and open-source models, but it is unclear how these models were selected.

Raw Abstract / Excerpt

Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for rigorous and realistic evaluations. However, most existing benchmarks evaluate agents in simplified, idealized settings. They typically rely on pre-packaged tool interfaces, overlook critical steps, and assume inputs are clean and fully specified. Consequently, they understate the difficulty of real deployments, where uncertainty and noise are ubiquitous and agents must proactively explore the environment to uncover new tools. To bridge this gap, we present AgentGym2, a new evaluation framework with task instances grounded in real-world end-to-end working demands. Beyond reasoning and planning, it measures agents' ability to execute end-to-end procedures, discover tools via exploration, compose tools for unseen tasks, and remain robust to noisy and underspecified information. Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2, revealing a substantial gap between the capability of current agents and the demands of real-world applications.