SwarmHarness: Skill-Based Task Routing via Decentralized Incentive-Aligned AI Agent Networks

2026-05-27

AI Summary

This work addresses the underutilization of abundant idle computational resources due to the absence of secure, trustworthy, and incentive-compatible sharing mechanisms. The authors propose a decentralized AI agent network that enables self-organized, coordination-free resource sharing through skill registration, utility-driven task routing, and a credit-based incentive mechanism grounded in approximate Shapley values. Innovatively integrating skill-oriented task routing, distributed hash table (DHT)-based node discovery, and scalable incentive computation, the framework establishes a self-regulating computational economy capable of emergent intelligence. Experimental results demonstrate that the system efficiently allocates tasks, fairly rewards contributions, and incentivizes nodes to autonomously specialize, thereby fostering collective, swarm-like collaborative behaviors.

Abstract

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.

Problem

• decentralized compute sharing
• incentive alignment
• idle GPU cycles
• distributed AI agents
• trustless resource pooling

Innovation

• decentralized compute sharing
• incentive mechanism
• Swarm intelligence
• task routing
• Shapley-value approximation