Contemporary urban systems are evolving under the influence of artificial intelligence (AI), advancing beyond automation into autonomous agency. Traditional smart city technologies have focused on operational efficiency through human-directed automation, but this study explores the rise of Agentic AI, AI systems capable of independently formulating and pursuing urban objectives. Urban sensing, enhanced by large language models, enables dynamic goal-setting and strategic adaptation, key to this agency. Employing a conceptual-methodological approach, the research integrates insights from Urban Studies, AI theory, and governance scholarship. Through critical literature review, conceptual mapping, and empirical analysis of platforms such as Alibaba’s City Brain and Citymind AI Agent. It identifies early agency indicators, such as goal reprioritisation, and proposes a typology distinguishing automation, autonomy, and agency. These findings suggest AI-driven urban ecosystems with partial decision-making autonomy, compelling a transformative shift in governance. This evolution demands a reassessment of regulatory, ethical, and planning frameworks, to ensure equitable and sustainable AI integration in urban environments via participatory governance and proactive regulation.

Cities play a crucial role in the planning of climate change adaptions. Although these actions are largely guided by global negotiations and national policies their consequences are usually felt by individual cities. Reconfiguring of urban infrastructure is the first step to ensure resilience to extreme weather events triggered by climate change. Many coastal cities are already begun to suffer because of climate change impacts; frequent flooding in Jeddah (Kingdom of Saudi Arabia) is an example. This paper attempts to investigate preparedness of urban water infrastructure in Jeddah for future climate change adaptions. It founds that the city has been lagging behind in action such as continuous & consistent reporting of relevant data, capacity building, research, education and awareness building, reconfiguration and expansion of grey & green infrastructure. We propose to formulate a three point policy for climate change adaption at local level with proper attention to grey, green and soft infrastructures.

Considering spatial accessibility of health services is a critical part in the planning and management of health services. There is evidence that poor geographical locations can obstruct prompt basic health care services to some population sections. We developed a location-allocation P-median model for health centres after analysing their sites, demand location of health services and the road network in Jeddah, Saudi Arabia. This model attempts to optimize health care services network and to put forward location recommendations to maximise service coverage. Our model is shown to be useful as it provides a robust evidence base to urban planners and policymakers responsible for making spatial decisions for the development of the health sector. Besides, it follows the paradigm of new urbanism that encourages decentralisation of essential facilities including basic healthcare in cities, where emphasis is on offering all basic services within walkable distances of 15 min. or less.

Saudi cities were expanded toward the outer areas in the past; that has produced urban sprawl which is solely dependent on private cars, as an outcome of cheaper gasoline. However, decreasing energy subsidies have compelled urban residents to rethink housing and transportation choices. This article attempts to explore the combined housing and transportation affordability in Jeddah. We collected primary data on housing and transport costs through an online survey in addition to the secondary data sources. The global Moran’s index and the local indicator of spatial association (LISA) were then used to explore the spatial clustering of combined affordability of housing and transportation. Moreover, a composite index was developed to identify future locations of district-level affordable housing. Results reveal that more than one quarter of the total respondents are willing to change their housing locations in the future; and housing and transportation (un)affordability is clustered at certain locations. This study recommends considering transportation and housing costs in an integrated way at the time of granting new planning permissions. The study emphasizes an immediate need for a rapid, affordable, and reliable public transportation system offering connectivity, as well as infill development in the areas that are affordable in terms of housing and transportation costs.

Urban systems are transforming as artificial intelligence (AI) evolves from automation to Agentic Urban AI (AI systems with autonomous goal-setting and decision-making capabilities), which independently define and pursue urban objectives. This shift necessitates reassessing governance, planning, and ethics. Using a conceptual-methodological approach, this study integrates urban studies, AI ethics, and governance theory. Through a literature review and case studies of platforms like Alibaba’s City Brain and CityMind AI Agent, it identifies early agency indicators, such as strategic adaptation and goal re-prioritisation. A typology distinguishing automation, autonomy, and agency clarifies AI-driven urban decision-making. Three trajectories are proposed: fully autonomous Agentic AI, collaborative Hybrid Urban Agency, and constrained Non-Agentic AI to mitigate ethical risks. The findings highlight the need for participatory, transparent governance to ensure democratic accountability and social equity in cognitive urban ecosystems.