Jul 2026· International Journal of Population Data Science· Vol 11· 0 citations
Medicine
TL;DR
The work that the UK Data Service has so far undertaken to move ODRL from a standard to an implementable set of metadata, workflows, and tools is outlined and how standards such as Data Use Ontology (DUO) and Data Privacy Vocabulary (DPV) complement ODRL within that context is described.
Abstract
In 2026, applying for access to data is still more complex and challenging for researchers than it should be. To some extent, this is because access & usage policies are still largely mediated by humans, compounded by a lack of alignment between organisations as to how access processes and workflows are implemented, with little supporting machine-actionable information to improve the situation through automation. This not only degrades the researcher experience, it inhibits progress on federating infrastructures for data that is not completely open. Open Digital Rights Language (ODRL) has been around for over a decade but is now gaining significant traction in a number of arenas as a mechanism to address the problem of machine-actionable access, usage, and rights management more generally. The recent and rapid advent of agentic AI is now making rights management for digital objects a priority issue that can no longer be deferred. “Access” is one of the key planks in the Cross-Domain Interoperability Framework (CDIF) and builds out implementable specifications for the “A” in FAIR. This presentation outlines the work that the UK Data Service has so far undertaken to move ODRL from a standard to an implementable set of metadata, workflows, and tools. We also articulate in detail how this is not merely a simple exercise in translating prose licences to RDF but requires a deeper understanding of the context in which “Access” operates. We also describe how standards such as Data Use Ontology (DUO) and Data Privacy Vocabulary (DPV) complement ODRL within that context.
Abstract—Many everyday business and personal tasks share a hidden structure: some input arrives, an intelligent decision needs to be made about it, and an output has to be produced quickly and repeatedly, at a scale no single person can sustain by hand. This paper presents a single, general-purpose AI workflow automation platform built around this idea, using n8n – a visual workflow automation engine – as the layer that actually thinks, decides, and adapts, while a conventional web application supplies the interface and storage around it. Rather than hard-coding every rule and every prompt into the backend, the platform externalises all of its decision logic into n8n, so that behaviour can be revised the moment real usage reveals a gap, without ever redeploying the application. The platform is demonstrated through two representative use cases built on the identical architecture: automated, conversational practice for a task that benefits from repeated rehearsal, and automated, personalised outreach for a task that benefits from scale. Both use cases were piloted informally, and both showed the same underlying pattern – consistent turnaround within seconds, and a steady improvement in outcome quality as the workflow layer was iterated on. This paper describes the architecture, the shared design rationale, the pilot results, and the limitations that inform future work.
Sanketh B M, T. Vasudev, Karthik R· International Journal of Res...· 0 citations
The ODRL policy language is emerging as the de-facto standard for policy modelling data access and usage preferences, AI governance policies and data workflows in European dataspaces. The current standard has no mathematical formal semantics to describe how a system should implement policy evaluation. This has resulted in a variety of systems and tools that implement their own interpretation of the language, which limits interoperability and cannot guarantee consistent results. Based on an existing semantic model of ODRL, we formalise the problems of ODRL evaluation for the access control and monitoring scenarios, in both static and streaming settings, and we provide a novel, efficient algorithm and implementation. We present the first ODRL Evaluator with transparent formal semantics and supporting all rule types. We experimentally measure its performance, analysing different scalability dimensions related to policy complexity and size of the data on which a policy is evaluated. We compare our system with the state-of-the-art by providing a comparative review of existing ODRL evaluators, which highlights the differences in supported ODRL features and evaluation modes.
Jaime Osvaldo Salas, Paolo Pareti, Adeel Aslam et al.· 0 citations
The integration of Artificial Intelligence (AI) into IT Operations Management (ITOM), commonly referred to as AIOps, offers substantial potential for automating workflows, enhancing efficiency, and supporting informed decision-making. However, practical implementation of AI within IT operations remains challenging, particularly due to data quality issues, the complexity of cloud-native environments, and skill gaps within operational teams. The emergence of Large Language Models (LLMs) presents new opportunities to address these barriers by leveraging their advanced natural language understanding, enabling the analysis of unstructured data such as logs, incident reports, and technical documentation. In this paper, we present an industry experience report conducted on Red Hat OpenShift to explore how LLMs can be operationalized in real-world Kubernetes-based environments. We integrate predictive machine learning models with LLM agents through tool-augmented reasoning, highlighting novel methods to automate IT tasks, enhance observability, and reduce operator burden. Our findings provide insights into both the capabilities and limitations of LLMs in production-grade AIOps scenarios.
Arthur Vitui, T. Chen· SIGSOFT FSE Companion· 0 citations
The swift growth of the open access (OA) sources has undergone a radical change in the sphere of scholarly communication, and more specifically, in the academic and research institutions in developing countries like India. The growing volume, heterogeneity, and complexity of OA material is increasingly putting pressure on libraries, which require effective and scalable management systems. Artificial intelligence (AI)-based automation can bring about the solution as it is able to provide automated metadata creation, smart information search, semantically structured information, and predictive analytics. In this paper, the future of open access resource management through the automation of AI is discussed in the light of Library and Information Science (LIS) in India. It evaluates the main AI applications used in OA repositories; the problems related to the implementation in the Indian context; and the ethical, infrastructural, and professional implications. The research focuses on the significance of aligning the policy, capacity building, and human supervision to make sure that integration of AI is conducive to the main principles of openness, inclusiveness, and equitable access. It is concluded in the paper that responsible implementation of AI-based automation has the potential to reinforce the open knowledge infrastructure in India dramatically, and increase the visibility of Indian scholarly output to the rest of the world.
Neelam Narang, Neetu Singh· International Journal of Inf...· 0 citations
LLM agents increasingly rely on tool calling to act on external systems, and the Model Context Protocol (MCP) has quickly become its de facto interface. Operating MCP at cloud scale, however, becomes difficult. On the tool provider side, legacy services are not directly callable through MCP; the rapid protocol development also creates ongoing compatibility cost. On the agent side, the number of accessible tool is limited by the LLM context window and inference overhead; mounting a large tool set increases token usage and inference latency and can reduce task success rate. Moreover, for stateful MCP backends with multiple replicas, preserving session affinity increases client-side complexity. We present a cloud-scale gateway system for MCP service. It breaks the direct-connect model on the data plane and offloads legacy service integration, consolidating incompatible MCP variants, access control, tool recommendation, and session-aware routing to the gateway. Hybrid retrieval sustains 98% Top-15 recall; it scales agent tool access to 3,000+ with high tool selection accuracy, and reduces tool selection time by $8.9\times$ and token usage by $23.8\times$, with low per-call overhead, stable under scale-out. Finally, we share the lessons learned from deploying the gateway system in production.
Mingxing Li, Enge Song, Yueshang Zuo et al.· 0 citations
The rapid diffusion of data‑driven automation and agentic AI systems is reshaping the foundations of work, decision‑making, and human–technology interaction. As organizations move toward Society 5.0— Japan’s vision for a human-centered “super smart” society in which cyber-physical intelligence augments human capability across economic and social systems—there is an urgent need for operational architectures that are not only technologically capable but also fundamentally human‑centric. This paper presents an applied model using Intelligent Operations framework that integrates agentic AI, enterprise data fabric, human‑in‑the‑loop governance, and secure multi‑system orchestration, and enterprise digital twins that simulate processes and operational states for context-aware decision support. The result is an adaptive socio‑technical system that enhances human decision‑making rather than replacing it, while simultaneously enabling automation at operational scale.The research builds on fieldwork across finance, supply chain, HR, and complex asset‑intensive environments, where organizational processes are distributed across heterogeneous platforms such as ERP, HCM, workflow systems, enterprise data lakes, RPA tools, and emerging AI orchestration layers. Traditional human‑computer interaction models are insufficient in these environments because workers face fragmented data landscapes, inconsistent process execution, and increasing cognitive load. The proposed Intelligent Operations framework addresses these pain points by introducing an orchestration layer that harmonizes data, interprets context (including real-time insights from digital twin models), and deploys agentic AI workers capable of completing multi‑step tasks across systems.A key contribution of this work is the definition of agentic AI in enterprise socio‑technical ecosystems—AI agents equipped not only with language models and planning capability but also with secure access to enterprise systems through structured patterns such as passthrough APIs, workflow orchestration, Model Context Protocol (MCP), and agent‑to‑agent (A2A) collaboration. Rather than relying on brittle rule‑based workflows, the agents dynamically interpret goals, assess context, and plan actionable sequences while maintaining traceability, decision lineage, and auditability. This supports a new form of “digital labor” that works alongside human employees to augment cognitive, administrative, and analytical tasks. However, the framework insists on human‑in‑the‑loop governance, recognizing that human oversight remains essential for ethical, safe, and responsible AI deployment. The DMO acts as a security and compliance boundary—enforcing identity controls, audit trails, approval checkpoints, policy enforcement, and anomaly detection throughout the agentic automation lifecycle. This hybrid model ensures that automation amplifies human capability without bypassing institutional safeguards or creating new forms of risk.The paper also discusses the human‑centric business implications: reduced cognitive load for knowledge workers, increased transparency of decision processes, improvements in cross‑functional collaboration, and the redefinition of roles as humans transition from transactional executors to supervisors, interpreters, and strategic actors. Proposed framework becomes the backbone for Society 5.0 organizational design—linking people, processes, data, and intelligent systems through a unified operational fabric.This research demonstrates that when designed with ergonomics, human values, and socio‑technical principles at the center, agentic AI become powerful enablers of human‑centric, resilient, and adaptive enterprises.
Elizabeth Koumpan, Laurentiu Gabriel Ghergu, Łukasz Strack et al.· AHFE International· 0 citations
