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 is about AgentCompass, an open-source evaluation infrastructure designed to evaluate Large Language Models (LLMs) as they evolve into autonomous agents. The infrastructure aims to address the current fragmented and redundant evaluation pipelines by offering a flexible, fault-tolerant, and transparent system for diagnosing failure modes. AgentCompass supports over 20 benchmarks across five capability dimensions and is intended to advance agent research.

What the Document Says

• AgentCompass is an open-source infrastructure for evaluating LLM-based agents.
• It consists of three independent components: Benchmark, Harness, and Environment.
• The infrastructure enables flexible configurations without reimplementing complex execution logic.
• It features a fault-tolerant asynchronous runtime and comprehensive trajectory analysis tools for diagnosing failure modes like reward-hacking.
• AgentCompass supports over 20 benchmarks across five capability dimensions.
• Its goal is to provide a scalable and reproducible infrastructure for advancing agent research.

Key Concepts Mentioned

• Large Language Models (LLMs)
• autonomous agents
• evaluation infrastructure
• AgentCompass
• Benchmark
• Harness
• Environment
• fault-tolerant asynchronous runtime
• trajectory analysis tools
• reward-hacking

Claims Made by the Document

• Current evaluation pipelines are fragmented and tightly coupled.
• AgentCompass addresses this issue by offering a flexible, fault-tolerant, and transparent system for diagnosing failure modes.
• AgentCompass supports over 20 benchmarks across five capability dimensions.

Evidence Used

• The text mentions the need for a unified evaluation infrastructure due to current fragmentation and redundancy in evaluation pipelines.
• The text describes AgentCompass's features that aim to address these issues.
• The text states that AgentCompass supports over 20 benchmarks.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

• What are the five capability dimensions supported by AgentCompass?

Notes for Future Q&A

May include interpretation. Labeled where it does.

• The text mentions five capability dimensions but does not explicitly list them.

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.