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AgentCompass addresses the critical fragmentation in evaluating autonomous LLM agents by introducing a unified infrastructure that separates benchmarks, harnesses, and environments. This modular approach enhances reproducibility and allows researchers to diagnose complex failure modes like reward-hacking without reimplementing execution logic. By supporting over 20 benchmarks across five capability dimensions, it provides a scalable foundation for advancing agent research.
AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
arXiv:2607.13705v3 Announce Type: replace Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
arXiv:2607.13705 [cs.AI]
(or arXiv:2607.13705v3 [cs.AI] for this version)
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