想解决城市服务碎片化问题？这个框架把LLM和代码、API、MCP接起来，跑通跨系统任务，成功率比最强基线高10个点。
UrbanAgent是一个面向跨系统城市任务的工具增强智能体框架，将大语言模型的推理能力与代码执行、API调用及Model Context Protocol工具集结合。研究团队同时推出了Urban-Eval基准，用于评估跨系统城市请求的任务结果与执行质量。实验显示UrbanAgent任务成功率达71%，比最强基线高出10个百分点，该优势在GPT-5-mini、Gemini-2.5-flash、DeepSeek-V4-flash和Qwen3-235B-A22B上保持一致。
UrbanAgent: A Tool-Augmented Agent for Cross-System Urban Tasks
Modern cities rely on an increasing number of digital services to operate, but residents' daily needs are still difficult to meet. Services are fragmented and have little interoperability, placing a heavy operational burden on users. Existing digital platforms, urban foundation models, and intelligent assistants each address only isolated aspects of an urban task. But they struggle to reliably convert complex natural-language requests into executable cross-system workflows. We propose Urban-Agent, a tool-augmented agent framework for cross-system urban tasks. It couples the cognitive and reasoning capabilities of a large language model with a tool-set supporting code execution, API calls, and Model Context Protocol. Through one adaptive closed loop, it clarifies missing information before acting, grounds tool use in live observations, and aligns the final response with observed evidence and task constraints. To address the evaluation gap, we introduce Urban-Eval, a benchmark specifically designed for cross-system urban request. Unlike prior benchmarks that assess either general tool use or urban knowledge and reasoning, Urban-Eval evaluates both task results and execution quality, including required tool coverage, dependency validity, and evidence traceability. Experimental results indicate that Urban-Agent reaches a 71% task success rate, 10 points above the strongest baseline. This lead holds across GPT-5-mini, Gemini-2.5-flash, DeepSeek-V4-flash, and Qwen3-235B-A22B.