UrbanAgent: A Tool-Augmented Agent for Cross-System Urban Tasks
A novel AI framework named UrbanAgent has been launched to tackle the disjointed nature of digital services in contemporary urban environments. This initiative is outlined in a paper available on arXiv (2608.03018), which integrates the reasoning power of a large language model with a suite of tools for code execution, API interactions, and the Model Context Protocol. UrbanAgent functions through a dynamic closed loop, ensuring that it gathers necessary information before taking action, bases tool utilization on real-time observations, and aligns its final outputs with the evidence and constraints of the task. The paper emphasizes that current digital platforms and intelligent assistants only manage fragmented urban tasks, lacking the ability to transform complex natural-language inquiries into actionable workflows across systems. UrbanAgent aims to fill this void by facilitating the smooth execution of tasks across various urban systems. Additionally, it addresses a gap in evaluation noted in the research, which falls under the categories of artificial intelligence and urban computing.
Key facts
- UrbanAgent is a tool-augmented agent framework for cross-system urban tasks.
- It couples a large language model with a tool-set supporting code execution, API calls, and Model Context Protocol.
- The framework uses an adaptive closed loop to clarify missing information, ground tool use, and align responses.
- It addresses the fragmentation of digital services in cities.
- Existing platforms, urban foundation models, and assistants only handle isolated aspects of urban tasks.
- The paper is available on arXiv with ID 2608.03018.
- The announcement type is 'new'.
- The paper identifies an evaluation gap in current systems.
Entities
Institutions
- arXiv