Smart city implementation increasingly relies on sensing and analytics; however, a persistent operational gap remains between anomaly detection and safe, timely, and accountable intervention in civil infrastructure systems. This paper proposes an Agentic AI-supported Digital Twin framework for smart city civil infrastructure management, where monitoring and action are linked and auditability is maintained. The Digital Twin continuously updates asset and network models of bridges, roads, and water infrastructure using multi-stream telemetry, incorporating state estimation, predictive maintenance, and what-if simulation services. At the orchestration layer, an agent-based Perception–Conceptualization–Action workflow implemented with LangChain and LangGraph enables cross-domain reasoning and coordinated mitigation planning through controlled API calls to municipal data. A permissioned blockchain cryptographically binds observations, approvals, and executed interventions, ensuring provenance, governance, and tamper evidence. To evaluate the framework, 18,000 incident simulations were conducted across five architectural configurations and three scenario complexity levels over 30 independent runs. This simulation study characterises framework behaviour under controlled stochastic conditions and does not constitute real-world operational validation. Ablation analysis isolates each component’s contribution, demonstrating that latency and mitigation gains are primarily attributable to multi-agent orchestration, while the blockchain layer drives decision auditability. Across all configurations, the fully agentic system substantially outperforms the rule-based baseline: mean detection latency of 3,197 s vs. 39,374 s, mitigation success rate of 66.2% vs. 45.5%, blockchain-anchored decision justification of 71.8% vs. 0%, and operator workload reduction of 91.7% vs. 0%. These results demonstrate that combining simulation-enabled digital twins with governance-aware agentic orchestration measurably improves response efficiency, recommendation quality, and action accountability within the bounds of a synthetic evaluation environment.

Artificial intelligence is reshaping employment in smart cities, yet centralized hiring platforms can deepen exclusion for persons with disabilities through privacy risk, biased models, weak multilingual support, and limited accommodation awareness. Because disability-related records are highly sensitive, no single institution holds enough representative data to train fair models, and centralizing such data is rarely permissible across borders. We propose FedAgent-Chain, a framework that integrates federated learning, blockchain-based auditability, multilingual processing, rule-based agentic services, and human-in-the-loop governance, extended with an education-to-employment module that builds individualized, accessible job-readiness pathways. Institutions across Saudi Arabia, the United States, China, and Europe train shared models without exchanging raw data. In a prototype evaluation on synthetic records over five seeds, the framework reached a mean F1 of 0.7207 (95% CI: [0.6506, 0.7909]), comparable to a centralized logistic-regression baseline while preserving data locality, with a formal $(\epsilon =3.2,\delta ={10}^{-5})$ differential-privacy guarantee after 20 rounds. Multi-dimensional fairness regularization lowered disability-category and work-mode disparity by 32.3% and 40.3% relative to local-only training. We report the fairness behavior transparently, including a case where the penalty does not outperform standard FedAvg on disability-category disparity, and we position cross-institutional integration with accountable governance, rather than raw metric superiority, as the central contribution.

Cities face pressure from urban growth and climate risk, yet deployed systems stay single-domain and reactive. This PRISMA-guided rapid review applies operationalized criteria to separate Agentic AI from conventional machine learning for SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). Agentic AI is defined by four properties: task-level autonomy, goal-directed planning, tool use, and multi-agent coordination; evidencing at least two marks a system as fully agentic. A two-tier search across five databases with backward citation tracking returned 896 records (2018–2026), of which 60 met the eligibility criteria and 14 satisfied the agentic threshold. The corpus is stratified by study type with a threshold sensitivity analysis. Two contributions follow: a reference architecture specifying how an agentic layer and an urban digital twin exchange state, and a real-data feasibility study on the SEVIR archive testing whether multimodal fusion improves hazard classification. On real data the proposed model is the best-ranked of four but only marginally exceeds a no-change persistence baseline, giving the assumption weak support, not operational evidence. The review reveals a field growing sharply since 2023, clustered in a few urban and climate domains, with almost no validated cross-domain deployment.

Rapid urbanization and intensifying climate risks are placing unprecedented pressure on cities to transition toward sustainable and resilient models. Achieving Sustainable Development Goals (SDGs) 11 (Sustainable Cities and Communities) and 13 (Climate Action) requires intelligent systems capable of interpreting complex urban dynamics and enabling proactive, adaptive decision-making. This paper presents a PRISMA-guided rapid review examining the role of Agentic Artificial Intelligence (AAI)–autonomous, goal-directed systems with multi-step reasoning, tool use, and multi-agent coordination–in advancing urban sustainability and climate resilience. Studies were required to exhibit at least two attributes: autonomous decision-making, multi-step planning, tool use or environmental interaction, and multi-agent coordination. From 920 records, 70 peer-reviewed studies were synthesized, covering smart mobility, infrastructure planning, waste management, emergency response, climate monitoring, emissions tracking, renewable energy forecasting, and multi-hazard early warning systems. Results show that despite rapid progress, AAI applications remain fragmented and domain-specific. To address this, a unified Agentic AI–Digital Twin framework is proposed, integrating real-time sensing, urban–climate co-simulation, multi-agent coordination, and adaptive decision intelligence. A Pareto-based optimization approach balances competing sustainability goals. Key challenges in interoperability, data governance, ethics, and scalability are identified, alongside a research roadmap for integrated intelligent urban ecosystems.

The present paper introduces ADAPT, an agentic artificial intelligence (AI) framework aimed at assisting people with disabilities and neurodivergent conditions to adopt healthier eating habits and organized daily life. It includes four specialized agents (meal planning, adaptive reminders, food guidance, and physiological monitoring), a Large Language Model (LLM)-based decision layer, a Model Context Protocol (MCP) routing layer, and a multimodal interface. Each agent is deployed in a Perception–Reasoning–Action (PRA) loop and communicates with each other using a shared blackboard and a hybrid reasoning component that uses rules and reinforcement learning. In addition to electronic health records (EHRs), nutritional databases, wearable and Internet of Things (IoT) devices, and smart kitchen gadgets, the framework includes modules for explainable AI (XAI) to provide understandable explanations to the user and the caregiver. A concrete use case is presented showing how the implemented prototype can help a neurodivergent person with diabetes by interpreting multimodal inputs, evaluating meal suitability, making real-time replacements, and providing sensory-sensitive prompts. Results of a synthetic simulation-based evaluation (500 simulated profiles, no real user evaluation) show a positive impact on nutritional adherence and reminder responsiveness, and usability was found to be appropriate across various accessibility profiles. The envisioned framework provides a more unified approach to assistive health technologies and a future path toward personalized, safe, and transparent digital health assistance for individuals with disabilities and neurodivergence.

The paper presents a detailed Agentic Artificial Intelligence (AI) model that would enable people with disabilities and neurodivergence to lead healthier lives and have more regular days. The system will use a multi-layer structure; it will include an Application and Interface Layer, an Agents Layer, and a Data Source Layer to provide adaptive, transparent, and inclusive support. Fundamentally, a hybrid reasoning engine will synchronize four special-purpose agents, which include: a personalized-nutrition-based, called a Meal Planner Agent; an adaptive-scheduling-based, called a Reminder Agent; interactive assistance during grocery shopping and cooking, called a Food Guidance Agent; and a continuous-intake-and-physiological-tracking, called a Monitoring Agent. All the agents interact through a central communicative system called the Blackboard/Event Bus, which allows autonomous interaction and real-time feedback loops with multimedia user interfaces. Privacy-sensitive data sources, including electronic health records (EHRs), nutritional databases, wearable sensors, and smart kitchen Internet of Things, are also included in the framework and placed into a policy-controlled layer, which ensures data safety and compliance with consent. Collaborative care and clinician dashboards allow common supervision, and discussable artificial intelligence (XAI) modules give brief explanations of why a decision was made, making users responsible and reliant. The proposed agentic AI framework is an extension beyond traditional assistive systems since it incorporates inclusiveness, personalization, and accessibility at all levels. It displays the intersection of multi-agent reasoning, multi-modal interfaces, and human-centered design that will enable the development of autonomy, health, and digital equity among people with disabilities and neurodivergence.