SORA-ATMAS: Adaptive trust management and multi-LLM aligned governance for future smart cities

The rapid evolution of smart cities relies on agentic AI for autonomous decision-making, yet introduces governance, risk, and compliance (GRC) challenges in decentralized environments. We propose SORA-ATMAS, an adaptive trust management and multi-LLM governance framework for smart-city disaster management. Evaluation with Weather, Traffic, and Safety agents shows the framework steers multiple LLMs (GPT, Grok, DeepSeek) toward policy-aligned outputs, reducing mean absolute error by 35% on average. Results demonstrate stable weather monitoring, effective handling of high-risk traffic plateaus (R ≈ 0.85), and adaptive trust regulation in safety scenarios. Runtime profiling confirms scalability, with throughput of 13.8–17.2 req/s, execution times  < 72 ms, and governance delays  < 100 ms for a 3-agent deployment; analytical projections indicate maintained performance at larger scales. Cross-domain policies ensure safe interoperability, such as allowing traffic rerouting only under validated weather conditions. SORA-ATMAS thus provides a regulation-aligned, verifiable governance framework that transforms distributed agent outputs into accountable, real-time decisions, offering a resilient foundation for smart-city management.