SwarmHarness 解决了闲置算力无法安全共享的核心痛点，做分布式计算或 AI 智能体网络的开发者可以直接参考其激励设计，无需依赖中心化云市场或复杂区块链。
SwarmHarness 提出了一种去中心化协议，让闲置的 GPU 算力（个人工作站、推理服务器、边缘设备）通过技能节点自组织成计算集群，无需中央协调或区块链基础设施。其核心组件包括基于分布式哈希表的节点发现、基于效用函数的任务路由，以及基于 Shapley 值的信用激励机制。节点通过贡献算力赚取信用，消耗信用提交任务，不贡献的节点信用耗尽后失去路由优先级，形成自调节的参与经济。该协议不仅用于算力共享，还作为自主分布式 AI 智能体网络的基础原语，支持智能体之间无需人工介入地雇佣算力、路由子任务和结算信用。
SwarmHarness: Skill-Based Task Routing via Decentralized Incentive-Aligned AI Agent Networks
Vast quantities of compute (GPU cycles on personal workstations, idle inference servers, and edge devices between jobs) go unused because no incentive-aligned protocol exists for their owners to share them safely and profitably. Existing approaches either require a trusted central coordinator (cloud marketplaces), demand heavy blockchain infrastructure (Golem, BrokerChain), or lack an incentive layer entirely (BOINC, Petals). We propose SwarmHarness, a decentralised protocol in which HarnessAPI skill nodes self-organise into a compute swarm without any central authority. SwarmHarness has three interlocking components: a SwarmRegistry built on a Distributed Hash Table (DHT) for peer discovery and capability advertisement; a SwarmRouter that dispatches tasks to nodes using a utility function over capability, load, latency, and trust; and SwarmCredit, an incentive mechanism that attributes compute-credit rewards to contributing nodes via a Shapley-value approximation. Nodes earn credits by serving tasks and spend credits to submit them; idle nodes that never contribute drain credits and lose routing priority, creating a self-regulating participation economy. As nodes specialise toward high-reward skills and routing signals act as digital pheromones, the network exhibits emergent collective intelligence analogous to biological swarms. Beyond compute sharing, SwarmHarness is a foundational primitive for autonomous distributed AI agent networks in which agents hire compute, route subtasks, and settle credits without human intermediation.