An integrated human‑automation teaming framework is presented that facilitates TDP development and supports cross‑disciplinary dialogue between designers, engineers, command staff, and policy‑makers and provides a structured basis for designing flexible and context‑appropriate adaptive automation in VUCA environments.
Abstract
Rapid advances in Information Technology (IT) and Artificial Intelligence (AI) have resulted in increasingly complex socio‑technical systems, placing new demands on human–automation collaboration. These demands are particularly acute in VUCA (Volatile, Uncertain, Complex, Ambiguous) environments, where failures can have immediate and severe consequences. Effective human‑automation teams must therefore adapt task and control allocation dynamically, while maintaining safety, accountability, and operator understanding. Adaptive automation (AA) has been widely studied as a means to support such flexibility, often described using Levels of Automation (LoA) frameworks. However, many existing LoA frameworks do not align well with decision‑cycle models commonly used to reason about responsibility distribution in VUCA contexts. This misalignment complicates the design and reuse of AA-solutions for real‑world applications. Team Design Patterns (TDPs) offer a promising approach by capturing reusable solutions to recurring challenges in human–automation teamwork. Yet, the systematic construction of coherent TDP sets remains difficult due to a lack of structured design support. To address this, we present an integrated human‑automation teaming framework that facilitates TDP development and supports cross‑disciplinary dialogue between designers, engineers, command staff, and policy‑makers. The framework extends NASA’s eight‑level LoA framework by assigning descriptive level names, explicit human‑loop status (in/on/out‑of‑the‑loop), and visual responsibility mapping inspired by the European Defence Agency’s methods‑of‑control framework. Based on a naval Uncrewed Surface Vessel use case, we developed and evaluated three TDPs with domain experts: uniform task delegation, uniform goal delegation, and a mixed‑level pattern combining different LoA across decision-cycle stages. Expert evaluation confirmed that mixed‑level TDPs best reflect operational, regulatory, and technological realities. Overall, the framework provides a structured basis for designing flexible and context‑appropriate adaptive automation in VUCA environments.
Modern computing systems exhibit increasing heterogeneity and often require runtime self-management and adaptation to cope with their structural and operational complexity, as well as changes in their environment and requirements. Self-Adaptive Software Systems (SASS) represent a class of context-aware and autonomous systems designed to manage such complexity. However, designing such systems remains challenging due to their complexity, runtime variability, and the continuous need to ensure functional and quality requirements. Large Language Models (LLMs) and Generative AI (Gen AI) offer promising capabilities, yet their use in the architectural design of SASS remains poorly understood. To that end, this study reports a work in progress systematic review. The review findings reveal that the use of LLMs and other Gen AI approaches for the architectural design of SASS remains nascent, with only four relevant studies identified. Across these studies, LLMs act as augmentative reasoning components, concentrated in the monitoring, analysis, planning, and knowledge phases of the MAPE-K loop and are only partially present in execution. Characteristics such as hybrid architectures, multi-agent reasoning, and retrieval-augmented grounding recur across the reviewed studies; however, given the small and heterogeneous evidence base, these are best viewed as preliminary observations rather than established trends, and trustworthiness and runtime assurance remain underexplored. As a work in progress, this paper contributes an initial characterization of LLM-supported design in self-adaptive systems, outlines research directions, and aims to stimulate discussion within the community on advancing LLM-supported architectural design for self-adaptive and autonomous software systems.
Nadeem Abbas, Nazia Shahzadi· WiPiEC Journal - Works in Pr...· 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
A Domain-Specific Language (DSL), named RI language, designed for the declarative description of microservices, along with a supporting tool, TSE (Toolbox Service Executor), implemented in Python and based on the RI grammar, enables the representation of structural and operational aspects of services in a technology-agnostic manner.
Cássio Ritse, Machado Dos, Santos Silva et al.· 0 citations
Design Systems (DS) help standardize front-end development, yet developers still face challenges when translating high-fidelity mockups into consistent, production-ready interfaces. Although AI-assisted tools have emerged as a potential solution, empirical evidence on their effectiveness within DS-centered workflows remains limited. This paper reports a controlled experiment conducted at a large Brazilian enterprise that compares manual development, DS-only development, and DS-aware AI-assisted development across Angular, iOS, and Android stacks. Results from two experimental cycles show that AI assistance significantly reduced time-to-delivery (by 46.7% to 69.4%), increased task completeness, and decreased performance variability. Analysis of break patterns further suggests reduced workflow friction and smoother task execution. These findings provide empirical evidence that DS-aware AI tools can significantly accelerate development, improve design fidelity, and yield practical benefits for industrial front-end workflows.
Luciane Silva, T. Rocha, Nicole Davila et al.· 0 citations
An interdisciplinary network of influencing factors is derived that makes explicit the dependencies between consistency mechanisms, organizational and technical conditions, and key agility outcomes such as responsiveness, transparency, and the realizability of development increments.
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The increasing availability of Artificial Intelligence (AI) tools has generated significant interest within the project management community; however, structured guidance tailored specifically to Project Managers remains limited. This paper proposes a standardized AI-enabled prompt architecture aligned with the PMBOK® 8E performance domains and processes. The framework consists of a Master Prompt, Task-Level Executable Prompts, and Refine Prompts designed to create bounded, context-aware interactions between Project Managers and AI systems. The proposed architecture embeds PMBOK-aligned terminology and follows the Inputs–Tools–Outputs (ITTO) logic. The AI system functions as an analytical and generative tool within this structure, processing structured and unstructured inputs—including expert judgment—and producing standardized outputs for managerial review and refinement. The architecture is designed to be generalized and extensible across all forty project management processes. This study does not present empirical performance metrics; rather, it introduces a structured conceptual framework intended for practical application and future validation. Practitioners are encouraged to implement the architecture in real project environments to evaluate measurable improvements and contribute to further academic development in AI-enabled project management.
Vittal Anantamula, Rajendra Harsh· 2026 6th International Confe...· 0 citations
