2026· IEEE Transactions on Cognitive Communications and Networking· Vol 12, pp. 10733-10747· 0 citations· 49 references
Abstract
Embodied Artificial Intelligence (AI) integrates multimodal large models into Embodied Agents (EAs), driving the evolution of Embodied Edge Intelligence Networks (EEINs) to handle the heterogeneous requests generated by EAs. To guarantee service performance for heterogeneous requests, Service Function Chain (SFC) orchestration has emerged as a critical solution, involving the sequential deployment of Network Functions (NFs) to satisfy customized service requirements. However, realizing SFC orchestration in EEINs presents several challenges, including limited forwarding performance, dynamic environment evolution, and high-dimensional decision spaces. To tackle these issues, we present A2ProSFC, an agentic AI-enabled SFC orchestration system that facilitates perception–reasoning–action loops by leveraging programmable switches. Specifically, we formulate a long-term SFC orchestration problem aimed at maximizing served SFC throughput while ensuring system load balancing. Subsequently, we employ Lyapunov optimization to decouple the long-term orchestration into a sequence of online optimization subproblems and design DiffOrch, a diffusion-based SFC orchestration algorithm. By leveraging In-band Network Telemetry (INT), DiffOrch perceives network state information and adaptively generates orchestration decisions. Furthermore, we design a pipeline integrating INT perception and SFC orchestration to validate the system’s effectiveness. Experimental results demonstrate that A2ProSFC improves throughput by 40.53% and enhances load balancing efficiency by 36.97% compared to existing baselines.
Cloud-edge computing environments are evolving rapidly, requiring orchestration mechanisms that may automatically construct and manage complex multi-step workflows with little human intervention. We introduce a framework for the agentic AI and how it should be able to orchestrate an autonomous end-to-end workload of cloud-edge enterprise infrastructures in general. The proposed framework relies on large language model (LLM)-driven agents capable of dynamic task decomposition, real-time decision-making, and self-correcting execution pipelines to manage heterogeneous workloads. Through the incorporation of multi-agent coordination protocols, context-aware scheduling algorithms, and feedback-driven optimization loops, the system facilitates seamless task delegation throughout edge nodes and cloud backend systems while managing latency, resource allocation, and compliance constraints. Experimental evaluations show up to percentage improvements in workflow completion rates, resource utilization, and fault tolerance over traditional static-command Rule-based orchestration approaches. Additionally, the framework features explainability modules and audit trails to promote transparency and accountability in autonomous operations. The results provide evidence that agentic AI architectures can serve as a scalable, resilient and intelligent control mechanism for next generation enterprise workflow management across hybrid cloud-edge settings. This has laid a foundation and is to our best of knowledge, the first systematic pioneers work that lays down a roadmap for production-grade autonomous orchestration deployed in analytics and enterprise domains.
Shiza Arshad, Anusha Joodala, A. Agade et al.· 2026 International Conferenc...· 0 citations
A Cognitive TN–NTN Service Orchestration framework that combines Digital Twins and Agentic Artificial Intelligence to achieve resilient network management and enables uninterrupted communication for smart cities, autonomous transportation, disaster recovery, industrial automation, maritime networks, and remote healthcare services is presented.
Siva Sudheer Mahadasu, Dr. Nazila Safavi· International Journal of AI...· 0 citations
The rapid development of large model-driven agent applications, such as digital assistants and robots, requires 6G radio access networks (RAN) to deliver enhanced flexibility, adaptability, and low-latency capabilities. However, the existing RAN user plane (UP) architecture suffers from coarse decoupling granularity and significant cross-layer functional redundancy. These limitations severely hinder the on-demand orchestration and dynamic reconfiguration required by heterogeneous agent services. To address these challenges, this paper proposes a ComBERT-driven service-based RAN UP decoupling method, specifically targeting the functional coupling and redundancy between the PDCP and RLC sublayers. First, we develop a domain-specific language model, ComBERT, by pre-training a BERT model on a 3GPP protocol corpus and fine-tuning it on text-matching tasks to deeply comprehend protocol semantics. Subsequently, ComBERT is utilized to extract semantic features from UP functional components, employing a sliding window mechanism to overcome truncation in lengthy protocol texts and using cosine similarity to measure functional relevance. Finally, a threshold-based fusion algorithm is designed to identify and merge cross-layer redundant functions, thereby forming independent service units with distinct responsibilities. These fused units serve as the basic building blocks for scenario-specific orchestration. Simulation results demonstrate that the proposed method reduces the number of UP components by 12.5%, 18.7%, and 18.2% in eMBB, URLLC, and mMTC scenarios, respectively. Simultaneously, it decreases average processing delays by 7.9%, 10.2%, and 11.0% across these respective scenarios. Ultimately, this approach effectively improves the lightweight deployment, processing efficiency, and reconfiguration capabilities of the service-based UP, providing a crucial foundation for on-demand service orchestration in 6G networks tailored to agent services.
Haiyu Ding, Shangyuan Du, Xin Sun et al.· Italian National Conference...· 0 citations
The main conclusion is that practical Agentic IoT depends less on placing an entire agent at one tier than on partitioning perception, memory, reasoning, and action under explicit latency, privacy, reliability, and safety constraints.
In Federated Learning (FL), the communication topology is a runtime variable rather than a fixed design choice, since links and edge devices drop in and out during training. Each round, the server must commit three coupled decisions, namely the communication topology, per-client resource allocation, and the aggregation rule for combining local updates. Recent agentic systems have begun bringing large language models (LLM) into FL, but the existing line of work either operates at setup time or handles a single runtime dimension such as client selection. We propose FL-MAESTRO, a multi-agent orchestrator that makes the joint runtime FL decision directly through three specialist LLM agents, one per decision dimension. A coordinator combines their analyses into a single decision, and a non-LLM feasibility check confirms it before the round executes. Because the orchestrator consumes the server's predicted-failure list, it withholds clients whose updates would never be aggregated, which removes the dominant source of wasted round energy in classical FL on volatile edge networks. Because client state is read as natural-text profiles, the same orchestrator extends to heterogeneous device classes without per-class energy models. On a non-IID CIFAR-10 benchmark, FL-MAESTRO matches the accuracy of the strongest energy-aware baseline while cutting wasted round energy from over a third to near zero. Code is available at https://github.com/denoslab/FL-MAESTRO.
Jiajun Wu, Zirui Wang, Jiayu Zhou et al.· 0 citations
EASy is proposed, a trainable agentic framework that jointly optimizes task performance and computational efficiency through reinforcement learning and consistently achieves stronger performance-efficiency trade-offs than strong agentic baselines.
Junnan Liu, Linhao Luo, Thuy-Trang Vu et al.· 0 citations