We are in a transformative era, and advances in Artificial Intelligence (AI), especially the foundational models, are constantly in the news. AI has been an integral part of many applications that rely on automation for service delivery, and one of them is mission-critical public safety applications. The problem with AI-oriented mission-critical applications is the humanin-the-loop system and the lack of adaptability to dynamic conditions while maintaining situational awareness. Agentic AI (AAI) has gained a lot of attention recently due to its ability to analyze textual data through a contextual lens while quickly adapting to conditions. In this context, this paper proposes an AAI framework for mission-critical applications. We propose a novel framework with a multi-layer architecture to realize the AAI. We also present a detailed implementation of AAI layer that bridges the gap between network infrastructure and missioncritical applications. Our preliminary analysis shows that the AAI reduces initial response time by 5.6 minutes on average, while alert generation time is reduced by 15.6 seconds on average and resource allocation is improved by up to 13.4%. We also show that the AAI methods improve the number of concurrent operations by 40, which reduces the recovery time by up to 5.2 minutes. Finally, we highlight some of the issues and challenges that need to be considered when implementing AAI frameworks.

翻译：我们正处于一个变革时代，人工智能（AI）特别是基础模型的进展不断成为新闻焦点。AI已成为许多依赖自动化服务交付的应用中不可或缺的组成部分，关键任务的公共安全应用便是其中之一。面向AI的关键任务应用存在的问题在于人机交互系统的局限性，以及在保持态势感知的同时缺乏对动态条件的适应能力。智能体人工智能（AAI）近期因其能够通过语境视角分析文本数据并快速适应环境变化而备受关注。在此背景下，本文提出了一种面向关键任务应用的AAI框架。我们设计了一种具有多层架构的创新框架来实现AAI，并详细阐述了连接网络基础设施与关键任务应用的AAI中间层实现方案。初步分析表明：AAI平均缩短初始响应时间5.6分钟，平均降低警报生成时间15.6秒，资源分配效率最高提升13.4%。同时，AAI方法将并发操作数量提升40项，使恢复时间最多减少5.2分钟。最后，我们指出了实施AAI框架时需考虑的关键问题与挑战。