AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Zichen Ding, Jiaye Ge, Shufan Jiang, Kai Chen, Mo Li, Qingqiu Li, Zehao Li, Zonglin Li, Tiaohao Liang, Shudong Liu, Zerun Ma, Zixing Shang, Wenhui Tian, Zun Wang, Liwei Wu, Zhenyu Wu, Jun Xu, Bowen Yang, Dingbo Yuan, Qi Zhang, Songyang Zhang, Peiheng Zhou, Dongsheng Zhu
• Venue: arXiv: cs.AI
• Published: 2026-07-15
• Collected: 2026-07-16
• URL: http://arxiv.org/abs/2607.13705v1
• PDF: https://arxiv.org/pdf/2607.13705v1
• DOI: UNKNOWN

Plain-Language Brief

The text discusses AgentCompass, an open-source infrastructure designed to evaluate Large Language Models (LLMs) as they transition into autonomous agents. The tool is lightweight, extensible, and organizes the evaluation process in three independent components: Benchmark, Harness, and Environment. It offers a fault-tolerant runtime and diagnostic tools for complex failures, and supports over 20 benchmarks across five capability dimensions.

What the Document Says

• AgentCompass is an open-source infrastructure for evaluating LLM-based agents
• It is lightweight and extensible
• The evaluation process is organized around three independent components: Benchmark, Harness, and Environment
• It offers a fault-tolerant asynchronous runtime
• It has comprehensive trajectory analysis tools for diagnosing failure modes
• AgentCompass supports over 20 benchmarks across five capability dimensions

Key Concepts Mentioned

• Large Language Models (LLMs)
• Autonomous agents
• Evaluation infrastructure
• Benchmark
• Harness
• Environment
• Fault-tolerant asynchronous runtime
• Trajectory analysis tools
• Failure modes

Claims Made by the Document

• The need for a unified evaluation infrastructure is critical as LLMs evolve into autonomous agents
• Current evaluation pipelines are fragmented and tightly coupled, hindering reproducibility and causing redundant engineering
• AgentCompass addresses these issues by offering an open-source, lightweight, extensible infrastructure for evaluating LLM-based agents

Evidence Used

• As Large Language Models (LLMs) evolve into autonomous agents...
• However, current evaluation pipelines remain highly fragmented and tightly coupled...
• To address this, we introduce AgentCompass...

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

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Limitations Noted by the Agent

Agent interpretation — not from the source.

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Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering. To address this, we introduce AgentCompass, an open-source, lightweight, and extensible infrastructure for evaluating LLM-based agents. AgentCompass organizes the evaluation process around three independent components, namely Benchmark, Harness, and Environment, thereby enabling flexible configurations without requiring the reimplementation of complex execution logic. Furthermore, it features a fault-tolerant asynchronous runtime and comprehensive trajectory analysis tools to transparently diagnose nuanced failure modes like reward-hacking. Natively supporting over 20 benchmarks across five capability dimensions, AgentCompass provides the community with a scalable and reproducible infrastructure for advancing agent research.