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arXiv · 2604.23080

Usable Agent Discovery for Decentralized AI Systems

Patrizio Dazzi·Emanuele Carlini·Matteo Mordacchini·Saul Urso
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Abstract

Large-scale agentic systems run on distributed infrastructures where many software agents share physical hosts and are discovered via peer-to-peer mechanisms. Discovery must handle node-level churn from failures and host departures and agent-level churn from demand-driven activation, deactivation, and state changes. Their interaction reshapes classic trade-offs between structured and unstructured overlays. We study decentralized agent discovery under this two-level churn, assuming nodes host multiple agents, overlays are structured or gossip-based, and agents switch between warm and cold states. Using Kademlia as a structured and Cyclon+Vicinity as a gossip baseline, we compare stable, node-churn-only, agent-cooling-only, and combined regimes to see when routing efficiency, resilience, and service readiness align or favor different designs. Structured overlays are more robust and efficient in stable and node-churn regimes, while gossip-based overlays remain competitive and can be faster when readiness dominates.

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Patrizio Dazzi, Emanuele Carlini, Matteo Mordacchini, Saul Urso. 2026-04-25. Usable Agent Discovery for Decentralized AI Systems. https://arxiv.org/abs/2604.23080

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