The rapid evolution of smart cities has increased the reliance on intelligent interconnected services to optimize infrastructure, resources, and citizen well-being. Agentic AI has emerged as a key enabler by supporting autonomous decision-making and adaptive coordination, allowing urban systems to respond in real time to dynamic conditions. Its benefits are evident in areas such as transportation, where the integration of traffic data, weather forecasts, and safety sensors enables dynamic rerouting and a faster response to hazards. However, its deployment across heterogeneous smart city ecosystems raises critical governance, risk, and compliance (GRC) challenges, including accountability, data privacy, and regulatory alignment within decentralized infrastructures. Evaluation of SORA-ATMAS with three domain agents (Weather, Traffic, and Safety) demonstrated that its governance policies, including a fallback mechanism for high-risk scenarios, effectively steer multiple LLMs (GPT, Grok, DeepSeek) towards domain-optimized, policy-aligned outputs, producing an average MAE reduction of 35% across agents. Results showed stable weather monitoring, effective handling of high-risk traffic plateaus 0.85, and adaptive trust regulation in Safety/Fire scenarios 0.65. Runtime profiling of a 3-agent deployment confirmed scalability, with throughput between 13.8-17.2 requests per second, execution times below 72~ms, and governance delays under 100 ms, analytical projections suggest maintained performance at larger scales. Cross-domain rules ensured safe interoperability, with traffic rerouting permitted only under validated weather conditions. These findings validate SORA-ATMAS as a regulation-aligned, context-aware, and verifiable governance framework that consolidates distributed agent outputs into accountable, real-time decisions, offering a resilient foundation for smart-city management.

翻译：智慧城市的快速发展增强了对智能互联服务的依赖，以优化基础设施、资源配置和居民福祉。代理式人工智能已成为关键赋能技术，通过支持自主决策与自适应协调，使城市系统能够实时响应动态条件。其优势在交通等领域尤为显著，交通数据、天气预报与安全传感器的融合实现了动态路径重规划和更快速的风险响应。然而，在异构的智慧城市生态系统中部署该技术引发了严峻的治理、风险与合规挑战，包括去中心化基础设施中的责任归属、数据隐私和监管协同问题。通过对三个领域代理（天气、交通与安全）的SORA-ATMAS评估表明，其治理策略（包括针对高风险场景的备用机制）能有效引导多个LLM（GPT、Grok、DeepSeek）生成领域优化且策略对齐的输出，各代理平均绝对误差降低35%。结果显示：天气监测保持稳定，高风险交通态势处理效能达0.85，安全/火灾场景的自适应信任调节值为0.65。三代理部署的运行时性能分析证实了可扩展性，吞吐量达13.8-17.2请求/秒，执行时间低于72毫秒，治理延迟小于100毫秒，分析预测表明在更大规模下仍能保持性能。跨领域规则确保了安全互操作性，例如仅在天气条件验证通过时才允许交通重规划。这些发现验证了SORA-ATMAS作为一个符合监管要求、具备情境感知能力且可验证的治理框架，能够将分布式代理输出整合为可追责的实时决策，为智慧城市管理提供稳健基础。