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

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 designed to test language models in realistic, complex scenarios that mimic real-world deployments. It highlights that most existing benchmarks do not fully represent the challenges faced in real-life applications due to their idealized nature and simplicity. The proposed framework evaluates agents' ability to execute end-to-end procedures, discover new tools through exploration, compose tools for unseen tasks, and remain robust to noisy and underspecified information.

What the Document Says

• Language agents are being deployed in real-world environments more frequently
• Most existing benchmarks evaluate agents in simplified and idealized settings
• AgentGym2 is a new evaluation framework that 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

Key Concepts Mentioned

• Language agents
• Evaluation framework
• Real-world deployments
• Simplified benchmarks
• End-to-end procedures
• Tool discovery
• Tool composition
• Robustness to noisy and underspecified information
• SOTA systems

Claims Made by the Document

• Most existing benchmarks do not fully represent the challenges faced in real-life applications
• AgentGym2 is a more realistic evaluation framework than existing ones
• Even SOTA systems struggle on AgentGym2

Evidence Used

• Description of the rapid deployment of language agents in production environments
• Comparison between simplified benchmarks and the proposed AgentGym2
• Experimental results showing struggles of SOTA systems on AgentGym2

Methods / Approach

• Developing a new evaluation framework (AgentGym2)
• Conducting experiments with 15 proprietary and open-source models

Limitations Stated by the Authors

Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

The paper does not provide details on the specific tasks or scenarios included in the AgentGym2 framework, nor does it explain how the exploration and tool composition aspects are implemented

Open Questions

• What specific tasks are included in the AgentGym2 framework?
• How is exploration and tool composition implemented in AgentGym2?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper focuses on presenting the new evaluation framework, but does not provide detailed information about its implementation. Further research could involve exploring the specific tasks and methods used in AgentGym2.

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.