Jul 2026· International Conference on Smart Communications and Networking· pp. 1-8· 0 citations· 27 references
Abstract
Network slicing exposes powerful configuration levers for latency, reliability, and capacity in 5 G and emerging 6G systems, but the resulting design space is difficult to navigate for non-experts. Even when a slice configuration is deployed, operators still need to monitor traffic behavior, detect drift from design intent, and iteratively adjust baselines as usage evolves. This paper presents Agent Slicer, an agentic AI system that turns natural language descriptions of diverse deployment environments, including stadiums, hospitals, campuses, and factories, into a complete closed-loop network slicing workflow. A user describes the site in plain language; a retrieval-augmented generator grounded in a curated master document corpus synthesizes a standards-aligned slice plan; a digital-twin streamer and temporal convolutional network (TCN) classifier produce realistic KPI streams and slice labels; a suite of slice analysis tools detects deviations between planned and observed behavior; and a re-planning module proposes updated slice baselines when sustained drift is observed. All of these tools are orchestrated by a LangGraph agent that maintains shared state, coordinates planning, simulation, monitoring, and re-planning phases, and keeps humans in the loop at critical decision points. The system is an agentic architecture that is applied across multiple network slicing scenarios by specializing the master document corpus and tool parameters for each domain, while reusing the same LangGraph backbone.
Integrated sensing and communications (ISAC) is moving from proof-of-concept demonstrations to system-level deployment in sixth-generation (6G) networks. Because sensing and communication share hardware, spectrum, and waveform resources, ISAC design now involves many tightly coupled choices, including waveform selection, sensing algorithm setup, resource scheduling, and deployment planning. This design space is already too large to manage well through manual tuning or isolated optimizers. This article introduces the \textit{Agent Compiler}, a large language model (LLM)-enabled compilation layer that translates high-level engineering intent into complete and executable ISAC system configurations. The Agent Compiler works in four stages: intent parsing, task decomposition, policy graph synthesis, and infrastructure mapping. It produces a verifiable intermediate representation called the ISAC Policy Graph (IPG). A runtime engine then deploys the compiled configuration and supports closed-loop adaptation at three levels: fast parameter updates, partial recompilation of affected subgraphs, and full workflow recompilation. The core design principle is strict time-scale separation: the LLM handles slow-loop strategic decisions, while proven algorithms retain real-time control in the fast loop. A UAV-assisted disaster rescue example illustrates the full compilation process. We also discuss open issues, including compilation latency, output reliability, constraint verification, and pipeline security, to guide future research.
Lijie Zheng, Xudong Zhong, Baoquan Ren et al.· 0 citations
This work organizes agentic workflows in a taxonomy and presents its first architectural characterization with a production study at Microsoft Azure and a controlled study of open-source frameworks, showing that agentic execution is fragmented and heterogeneous.
Jirong Yang, Peizhe Liu, Chaojie Zhang et al.· 2 citations
NetConfArena is presented, an executable benchmark for evaluating LLM agents in closed-loop network configuration, and its findings suggest two future directions: using validated trajectories as supervision signals to improve foundation models, and designing harness mechanisms that make agent execution more reliable and accountable.
Chang Liu, Xiaohui Xie, Xinyi Chen et al.· 0 citations
TraceBrain transforms the execution history into a reusable operational memory substrate, bridging runtime observability with retrieval-driven agent adaptation, and bridging runtime observability with retrieval-driven agent adaptation.
This work presents DeepEye, a workflow-centric agentic data system that turns user intents into transparent and steerable analytical workflows and develops DataMagic as the system’s Video Generator, a declarative multi-agent method that improves data-video quality.
This work presents Aries, a full-stack experimentation framework that separates task semantics from execution configurations, reconstructs cross-component agent trajectories with correlated system telemetry, and exposes stateful tool execution through a consistent interface across heterogeneous sandbox substrates.
Leonid Kondrashov, Hongrui Liu, JooYoung Park et al.· 1 citation