Industry 5.0 envisions a cyber–physical future where humans and robots collaborate harmoniously, empowered by 6G connectivity and intelligent automation. Central to this vision is the ability to autonomously configure complex production pipelines based on diverse and evolving human intents. Existing orchestration technologies exhibit critical shortcomings in terms of self-learning, validation, error diagnosis, and rectification capabilities. To this end, we propose an Agentic AI orchestration framework that interprets human intents and dynamically assembles optimal technology pipelines using a self-improving, retrieval-augmented large language model (LLM) and a Bayesian contextual-bandit selector. This enables dynamic adaptation in unpredictable factory environments. Our solution is validated in a cyber–physical testbed integrating digital twins (DTs), distributed AI, robotics, and real-world network infrastructure. Compared to baseline LLMs, our system reduces orchestration iterations by over 94% for a given intent and by around 90% for an unseen intent, showing rapid convergence and strong generalization. Real-world deployments mirror DT results, confirming both the fidelity of the simulation and the practical value of intent-driven orchestration for human-centric manufacturing.

The Graph Neural Network-based prediction models have demonstrated remarkable utility in traffic prediction, and their efficacy is highly determined by the quality of the provided graphs. Consequently, there is an increasing demand for employing Graph Structure Learning (GSL) techniques to optimize or generate the graphs. However, existing GSL techniques for traffic prediction encounter various issues, including the absence of temporal dynamicity, noisy connections, and insufficient supervisory information. To address these limitations, this paper proposes a novel two-stage graph generation framework called Contrastive Learning-based Graph Structure Denoising Network (CLGSDN). This framework formulates the graph generation task as a probabilistic observation-inference process: using self-learning adjacency matrix and Time Delayed Self-Attention (TDSA) methods to generate a series of graph observations, then inferring the optimal graph based on observations. The self-learning adjacency matrix is responsible for learning all potential connections in the graph, while TDSA enables the graph to change with traffic flow. In addition, CLGSDN identifies and eliminates noisy connections by modeling negative samples of the graph (edges), and defines virtual labels to achieve Spatiotemporal Graph Contrastive Learning (ST-GCL) in traffic prediction. The experimental results show that CLGSDN significantly enhances current mainstream traffic prediction models by providing reliable and efficient graphs. As such, it has significant implications for a wide range of applications, including traffic management, logistics, and smart transportation systems.