Real-Time AI Service Economy: A Framework for Agentic Computing Across the Continuum

2026-03-05

AI Summary

This study addresses the challenge of achieving stable and scalable decentralized resource allocation for autonomous AI agents operating across the device-edge-cloud continuum, where complex service dependencies, resource contention, and governance constraints hinder real-time performance. To overcome this, the authors propose a hybrid orchestration architecture that encapsulates intricate subgraphs of the service dependency directed acyclic graph (DAG) into structured resource slices, thereby simplifying market interactions while preserving efficiency and stability. Theoretical analysis and 1,620 experiments demonstrate that this approach reduces price volatility by 70–75% and enables decentralized markets to closely approximate the optimality of centralized allocation, all while maintaining throughput. This work is the first to reveal the critical influence of dependency graph topology on price stability and pioneers the use of resource slice encapsulation to enhance market scalability.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Real-time AI services increasingly operate across the device-edge-cloud continuum, where autonomous AI agents generate latency-sensitive workloads, orchestrate multi-stage processing pipelines, and compete for shared resources under policy and governance constraints. This article shows that the structure of service-dependency graphs, modelled as DAGs whose nodes represent compute stages and whose edges encode execution ordering, is a primary determinant of whether decentralised, price-based resource allocation can work reliably at scale. When dependency graphs are hierarchical (tree or series-parallel), prices converge to stable equilibria, optimal allocations can be computed efficiently, and under appropriate mechanism design (with quasilinear utilities and discrete slice items), agents have no incentive to misreport their valuations within each decision epoch. When dependencies are more complex, with cross-cutting ties between pipeline stages, prices oscillate, allocation quality degrades, and the system becomes difficult to manage. To bridge this gap, we propose a hybrid management architecture in which cross-domain integrators encapsulate complex sub-graphs into resource slices that present a simpler, well-structured interface to the rest of the market. A systematic ablation study across six experiments (1,620 runs, 10 seeds each) confirms that (i) dependency-graph topology is a first-order determinant of price stability and scalability,(ii) the hybrid architecture reduces price volatility by up to 70-75% without sacrificing throughput, (iii) governance constraints create quantifiable efficiency-compliance trade-offs that depend jointly on topology and load, and (iv) under truthful bidding the decentralised market matches a centralised value-optimal baseline, confirming that decentralised coordination can replicate centralised allocation quality.

Problem

• Real-Time AI Services
• Dependency Graphs
• Resource Allocation
• Price Stability
• Decentralized Coordination

Innovation

• dependency-graph topology
• agentic computing
• hybrid resource allocation
• price stability
• real-time AI services