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Machine Learning

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

Zhiheng Xi, Dingwen Yang

Featured July 14, 2026

Original Paper

AI-generated analysis — This is SciGrove's AI interpretation of the paper, not peer-reviewed content. Always refer to the original paper.

Simply

A new testing ground called AgentGym2 helps show that even the smartest AI agents struggle with messy, real-world tasks because they need to find their own tools and deal with confusing information.

In depth
AgentGym2 introduces a novel evaluation framework for Large Language Model (LLM) agents, moving beyond idealized settings to assess capabilities in de-idealized real-world environments. It measures agents' ability to perform end-to-end procedures, proactively discover and compose tools, and maintain robustness against noisy and underspecified information, revealing significant gaps in current state-of-the-art models.

Key Takeaways

  • 1
    AgentGym2 provides a de-idealized evaluation framework for LLM agents, simulating real-world complexities like noisy inputs and tool discovery.
  • 2
    The benchmark covers end-to-end task completion across diverse scenarios, including complex tool use, data analysis, and deep search.
  • 3
    Experiments show that even state-of-the-art LLM agents struggle significantly on AgentGym2, highlighting a gap in real-world readiness.

Conceptual Flow

HIGH LEVEL
1
Methodology: Testing Agents in the Real World

Instead of easy, fake tests, this system makes AI agents solve hard problems like real people do, using basic tools and figuring things out on their own.

AI Agent
Uses
Basic Tools
Real World Tasks
Performance Score
2
Results: Current AI Agents Struggle

The tests showed that even the best AI agents often fail at these real-world challenges, meaning they still have a lot to learn before being truly helpful.

Old Easy Tests
New Hard Tests
Show
High AI Scores
Low AI Scores

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