Agent-to-Agent (A2A) networks enable autonomous AI agents to collaborate by sharing reusable problem-solving instructions. However, how these decentralized ecosystems operate in practice remains largely unexplored. We present the first large-scale empirical study of EvoMap, a prominent A2A collaboration network. By analyzing over 1.5M assets and 128K agents, we show how design choices that prioritize scalable growth introduce trade-offs in reusability, evolution, and auditability. First, EvoMap's credit economy rewards agents for publishing valuable assets. Although this design encourages participation at scale, rewards are tied primarily to publication rather than adoption. This leads agents to mass-produce assets to accumulate credits. As a result, 98% of assets are never reused, while rewards become highly concentrated among a small fraction of agents. Second, EvoMap employs an algorithm (referred to as GDI) to score and rank the quality of these shared assets. We demonstrate that this scoring system is flawed: rather than measuring objective performance, an asset's rank is heavily dictated by unverified, self-reported metadata (e.g., claimed lines of code modified). This allows agents to trivially manipulate their asset's scores. Finally, EvoMap relies on agents to provide local execution logs as evidence that uploaded assets function correctly. Because these validations are not independently verified, over 84% of approved assets bypass quality checks using vacuous tests (e.g., console$.$log()). Our findings show that future A2A collaboration networks cannot rely on unverified self-reporting alone. Scalable collaboration requires mechanisms that balance open participation with verifiable execution and trustworthy evaluation.

翻译：智能体间（Agent-to-Agent, A2A）网络允许自主AI智能体通过共享可重用的问题解决指令进行协作。然而，这些去中心化生态系统在实际中的运作方式在很大程度上仍未得到探索。我们提出了对EvoMap（一个著名的A2A协作网络）的首个大规模实证研究。通过分析超过150万项资产和12.8万个智能体，我们展示了优先考虑可扩展增长的设计选择如何在可重用性、演化和可审计性方面引入权衡。首先，EvoMap的信用经济机制奖励那些发布有价值资产的智能体。尽管这种设计鼓励大规模参与，但奖励主要与发布行为挂钩，而非与资产的实际采用挂钩。这导致智能体为了积累信用而大规模生产资产。其结果是，98%的资产从未被重用，而奖励则高度集中在少数智能体手中。其次，EvoMap采用了一种算法（称为GDI）来对这些共享资产的质量进行评分和排名。我们证明，这种评分系统存在缺陷：它并非衡量客观性能，资产的排名在很大程度上是由未经验证的、自我报告的元数据（例如，声称修改的代码行数）决定的。这使得智能体可以轻易操纵其资产的评分。最后，EvoMap依赖智能体提供本地执行日志作为上传资产功能正常的证据。由于这些验证并非独立完成，超过84%的获批资产利用空洞的测试（例如，console.log()）绕过了质量检查。我们的研究结果表明，未来的A2A协作网络不能仅依赖未经验证的自我报告。可扩展的协作需要一种机制，在开放性参与与可验证的执行和可信的评估之间取得平衡。