[2606.13608] AgentBeats: Agentifying Agent Assessment for Openness, Standardization, and Reproducibility

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Computer Science > Artificial Intelligence

arXiv:2606.13608 (cs)

[Submitted on 11 Jun 2026]

Title:AgentBeats: Agentifying Agent Assessment for Openness, Standardization, and Reproducibility

Authors:Xiaoyuan Liu, Jianhong Tu, Yuqi Chen, Siyuan Xie, Sihan Ren, Tianneng Shi, Gal Gantar, Evan Sandoval, Donghyun Lee, Daniel Miao, Peter J. Gilbert, Nick Hynes, Mauro Staver, Warren He, David Marn, Andrew Low, Xi Zhang, Elron Bandel, Michal Shmueli-Scheuer, Siva Reddy, Alexandre Drouin, Alexandre Lacoste, Ramayya Krishnan, Elham Tabassi, Yu Su, Victor Barres, Chenguang Wang, Wenbo Guo, Dawn Song<br>View a PDF of the paper titled AgentBeats: Agentifying Agent Assessment for Openness, Standardization, and Reproducibility, by Xiaoyuan Liu and 28 other authors

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Abstract:Agent systems are advancing quickly across domains, but their evaluation remains fragmented. Most benchmarks rely on fixed, LLM-centric harnesses that require heavy integration, create test-production mismatch, and limit fair comparison across diverse agent designs. The root problem is the lack of an open, agent-agnostic assessment interface. We advocate Agentified Agent Assessment (AAA), where evaluation is performed by judge agents and all participants interact through standardized protocols: A2A for task management and MCP for tool access. Conventional benchmarking defines two separate interfaces, one for the benchmark and one for the agent, while AAA only needs one; this yields a generic, unified framework that separates assessment logic from agent implementation and enables reproducible, interoperable, and multi-agent evaluation. We further introduce AgentBeats as a concrete realization of AAA: we identify five practical operation modes that make standardized assessment compatible with real-world constraints on openness, privacy, and reproducibility.

To evaluate our design at scale, we conduct two studies: a five-month open competition that drew 298 judge agents across 12 categories together with 467 subject agents from independent participants, showing that AAA applies across a heterogeneous range of benchmarks; and a case study on coding agents that confirms agentified evaluation preserves fidelity with the public record while surfacing previously missing head-to-head results, yielding research insights about agent design. Combining a community-scale field study and a controlled coding case study, we verify that AAA delivers coverage, practicality, and fidelity across heterogeneous scenarios at scale. Together, AAA and AgentBeats offer a clear path toward open, standardized, and reproducible agent assessment.

Subjects:

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

Cite as:<br>arXiv:2606.13608 [cs.AI]

(or<br>arXiv:2606.13608v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.13608

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Xiaoyuan Liu [view email]<br>[v1]<br>Thu, 11 Jun 2026 17:23:54 UTC (454 KB)

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