The Cost of Saying Hello: An Empirical Study of Handshake Overhead in AI Agent Communication Protocols
arXiv cs.AIen
arXiv:2610.30151v1 Announce Type: new Abstract: Every interaction between AI agents begins with a handshake: the initiating agent must locate its counterpart, learn what it can do, and establish a session before the first useful message can be sent. Surveys of agent communication protocols name handshake overhead as an efficiency criterion, yet none report measurements. This paper presents the first empirical, phase-decomposed benchmark of the handshake in three major agent protocols: MCP (in both its 2025 initialize and 2026 discover eras), A2A, and ACP. Using each protocol's official SDK, we instrument every request for round trips, per-phase latency, wire bytes, and token cost across cold
This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.
Read the full story at arXiv cs.AI- Forskning
- Agenter
Related AI news
- RoleFlow: Self-Evolving Multi-Role Agentic Collaboration via Structural AdaptationarXiv cs.AI · October 23, 2026
- Soft Ontology Priors over Latent Concept Posteriors for Weakly Supervised Neuro-Symbolic LearningarXiv cs.AI · October 23, 2026
- When Stable and Preferred Semantics Agree: A Path-Based ApproacharXiv cs.AI · October 23, 2026
- Route-and-Generate: KL-Regularized Router Improvement for Reasoning Routing in LLMsarXiv cs.AI · October 23, 2026
- Diagnosing Personalization Failures in Mobile GUI AgentsarXiv cs.AI · October 23, 2026
- Better Reasoning, Not Just Bigger Models: A Structure-Grounded Framework for Efficient and Reliable InferencearXiv cs.AI · October 23, 2026