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

Problem
The rapid advancement of agent systems across various domains has led to fragmented evaluation methodologies, primarily reliant on fixed, LLM-centric benchmarks that necessitate extensive integration and often result in test-production mismatches. This paper identifies a critical gap in the literature: the absence of an open, agent-agnostic assessment interface that facilitates fair comparisons among diverse agent designs. The authors propose a novel framework, Agentified Agent Assessment (AAA), to address these issues. This work is presented as a preprint and has not undergone peer review.

Method
The core technical contribution of this paper is the AAA framework, which allows for agent evaluation through judge agents interacting via standardized protocols: A2A (Agent-to-Agent) for task management and MCP (Multi-Channel Protocol) for tool access. Unlike conventional benchmarking that employs separate interfaces for benchmarks and agents, AAA consolidates these into a single interface, thereby decoupling assessment logic from agent implementation. This design promotes reproducibility, interoperability, and multi-agent evaluation. The authors introduce AgentBeats as a practical realization of AAA, detailing five operational modes that align standardized assessment with real-world constraints, including openness, privacy, and reproducibility.

Results
To validate the AAA framework, the authors conducted two significant studies. The first was a five-month open competition involving 298 judge agents across 12 categories and 467 subject agents from independent participants. This study demonstrated that AAA is applicable across a diverse range of benchmarks. The second study focused on coding agents, revealing that agentified evaluation maintains fidelity with public records while uncovering previously unreported head-to-head results. The results indicate that AAA effectively delivers coverage, practicality, and fidelity across heterogeneous scenarios at scale, although specific quantitative metrics are not disclosed in the abstract.

Limitations
The authors acknowledge that while AAA and AgentBeats provide a robust framework for agent assessment, the implementation may still face challenges related to the integration of diverse agent designs and the potential for biases in judge agent evaluations. Additionally, the reliance on community participation in the open competition may introduce variability in the quality of subject agents. The paper does not address the scalability of the framework in terms of computational resources required for large-scale evaluations.

Why it matters
The introduction of AAA and AgentBeats represents a significant advancement in the evaluation of agent systems, providing a structured approach that enhances openness, standardization, and reproducibility in agent assessments. This framework has the potential to streamline future research and development in agent design, enabling more reliable comparisons and insights into agent performance. The implications of this work are substantial for the field, as it lays the groundwork for future studies to build upon standardized evaluation methods, fostering a more collaborative and transparent research environment, as published in arXiv cs.AI.