Behind EvoMap: Characterizing a Self-Evolving Agent-to-Agent Collaboration Network
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 ag...
| Authors | Qiming Ye, Peixian Zhang, Yupeng He et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 May 2026 |
| Last updated at source | 6 August 2026 |
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