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

• URL: http://arxiv.org/abs/2603.05614v1
• Date: Thu, 05 Mar 2026 19:14:54 GMT
• Title: Real-Time AI Service Economy: A Framework for Agentic Computing Across the Continuum
• Abstract summary: This article shows that the structure of service-dependency graphs, modelled as DAGs, is a primary determinant of whether decentralised, price-based resource allocation can work reliably at scale.<n>We propose a hybrid management architecture in which cross-domain sub-graphs into resource slices that present a simpler, well-structured interface to the rest of the market.
• Score: 10.35088918434398
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• The Illusion of Multi-Agent Advantage [63.92466143571383]
  We show that expert-architected Multi-Agent Systems (MAS) consistently outperforms automatically generated architectures in raw performance and cost-efficiency.<n>Existing evaluation frameworks mask critical architectural gaps and inefficiencies by failing to account for the marginal utility of increased computational cost.
  arXiv  Detail & Related papers  (2026-06-11T07:39:24Z)
• Edge-AI-Driven Learning-to-Rank for Decentralized Task Allocation in Circular Smart Manufacturing [54.4933359656657]
  This work proposes an Edge-AI-driven decentralized task allocation framework based on ranking-aware negotiation.<n>A ranking-aware formulation finally reshapes the learning objective to align with the ordering-based nature of winner selection.<n>Results show improved delay and deadline adherence under high load, and enhanced energy efficiency under tighter constraints.
  arXiv  Detail & Related papers  (2026-05-14T20:38:25Z)
• Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach [36.3035973200471]
  It is imperative for AIGC service providers to strategically schedule AIGC workloads to reduce data center energy costs.<n>We propose a joint energy management and coordinated AIGC workload scheduling framework to tackle these challenges.<n>A system utility problem is formulated to balance AIGC service revenue with operational penalties and costs.
  arXiv  Detail & Related papers  (2026-05-03T09:17:29Z)
• Karma Mechanisms for Decentralised, Cooperative Multi Agent Path Finding [7.187733825162918]
  Multi-Agent Path Finding (MAPF) is a coordination problem in large-scale robotic and cyber-physical systems.<n>This paper proposes a decentralised coordination framework for cooperative MAPF based on Karma mechanisms.
  arXiv  Detail & Related papers  (2026-04-09T08:35:13Z)
• DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning [62.347535250646196]
  This paper proposes DeFRiS, a Decentralized Federated Reinforcement Learning framework for robust and scalable Silo-cooperative IoT application scheduling.<n>DeFRiS integrates three innovations: (i) an action-space-agnostic policy utilizing candidate resource scoring to enable seamless knowledge transfer across heterogeneous silos; (ii) a silo-optimized local learning mechanism combining Generalized Advantage Estimation with clipped policy updates to resolve sparse delayed reward challenges; and (iii) a Dual-Track Non-IID robust decentralized aggregation protocol leveraging gradient fingerprints for similarity-aware knowledge transfer and anomaly detection.
  arXiv  Detail & Related papers  (2026-03-16T02:02:38Z)
• The Headless Firm: How AI Reshapes Enterprise Boundaries [5.856292656853395]
  We argue that agentic AI induces a structural change in how coordination costs scale.<n>We formalize this claim as a coordination cost model with two falsifiable empirical predictions.
  arXiv  Detail & Related papers  (2026-02-24T22:13:14Z)
• Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets [41.99844472131922]
  Decentralized agents can approximate optimal coordination in local energy markets without explicit peer-to-peer communication.<n>Stigmergic signaling provides sufficient context for complex grid coordination, offering a robust, privacy-preserving alternative to expensive centralized communication infrastructure.
  arXiv  Detail & Related papers  (2026-02-17T22:22:32Z)
• Mechanism-Based Intelligence (MBI): Differentiable Incentives for Rational Coordination and Guaranteed Alignment in Multi-Agent Systems [0.0]
  I introduce Mechanism-Based Intelligence (MBI), a paradigm that reconceptualizes intelligence as emergent from the coordination of multiple "brains", rather than a single one.<n>It provides a provably efficient, auditable and generalizable approach to coordinated, trustworthy and scalable multi-agent intelligence grounded in economic principles.
  arXiv  Detail & Related papers  (2025-12-22T22:22:13Z)
• Towards a Science of Scaling Agent Systems [79.64446272302287]
  We formalize a definition for agent evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, modelic, and task properties.<n>We derive a predictive model using coordination metrics, that cross-validated R2=0, enabling prediction on unseen task domains.<n>We identify three effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead, and (2) a capability saturation: coordination yields diminishing or negative returns once single-agent baselines exceed 45%.
  arXiv  Detail & Related papers  (2025-12-09T06:52:21Z)
• Market-Driven Subset Selection for Budgeted Training [1.7969777786551429]
  We propose a market-based framework that treats each training example as a tradeable contract.<n>On GSM8K mathematical reasoning under strict 60k-token budgets, our selector achieves parity with strong single-signal baselines.<n>Our framework unifies multi-signal data curation under fixed computational budgets for prompt-level reasoning and classification tasks.
  arXiv  Detail & Related papers  (2025-10-02T18:12:03Z)
• Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation [55.47971671635531]
  Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA)<n>Retrieval-Augmented Generation (RAG) addresses this limitation by enriching LLMs with external knowledge.<n>Existing systems primarily rely on unstructured documents, while largely overlooking relational databases.
  arXiv  Detail & Related papers  (2025-09-30T22:19:44Z)
• PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis [75.14189839277928]
  We present PowerGrow, a co-generative framework that significantly reduces computational overhead while maintaining operational validity.<n> Experiments across benchmark settings show that PowerGrow outperforms prior diffusion models in fidelity and diversity.<n>This demonstrates its ability to generate operationally valid and realistic power grid scenarios.
  arXiv  Detail & Related papers  (2025-08-29T01:47:27Z)
• Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT [106.83952081124195]
  Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. We propose an adaptive ADMM (asI-ADMM) algorithm and apply it to decentralized RL with edge-computing-empowered IIoT networks. Experiment results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability, and can well adapt to complex IoT environments.
  arXiv  Detail & Related papers  (2021-06-30T16:49:07Z)
• F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning [110.35516334788687]
  Decentralized multi-agent reinforcement learning algorithms are sometimes unpractical in complicated applications. We propose a flexible fully decentralized actor-critic MARL framework, which can handle large-scale general cooperative multi-agent setting. Our framework can achieve scalability and stability for large-scale environment and reduce information transmission.
  arXiv  Detail & Related papers  (2020-04-17T14:56:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.