Agentic AI can support unmanned aerial vehicle (UAV) autonomy by providing high-level recovery reasoning when local waypoint- or setpoint-based execution encounters blocked passages, repeated no-progress behavior, or mission-level ambiguity. On physical UAVs, however, remote reasoning is most useful when it is invoked selectively, since each call introduces latency, resource cost, backend uncertainty, and a need to validate the returned decision. This paper presents Persistent Mission Runtime (PMR), a UAV recovery framework that keeps the mission loop and safety-critical execution local while using an external agentic reasoner only as an on-demand recovery module. The reasoner selects from predefined recovery skills, and each returned decision is parsed, verified, safety-filtered, and mapped to local executor actions before it can affect flight. PMR introduces learned Cognitive Value of Invocation (learned-CVI), a compact admission gate that estimates when remote agentic reasoning is likely to improve near-term mission progress enough to justify its operational cost. Across a fixed 400-run Gazebo/PX4 benchmark with eight scenarios, learned-CVI raises hard/ambiguous-regime success from 5.0% under local-only autonomy to 95.0%, outperforms one-shot and periodic reasoning baselines by 20.0 and 32.5 percentage points, and reduces remote-agent calls by 16.7% and logged tokens by 29.2% relative to a manually tuned rule-based invocation baseline.

翻译：智能体AI可通过在基于航点或设定值的局部执行遇到通道阻塞、重复无进展行为或任务级歧义时提供高级恢复推理，从而支持无人机自主性。然而在物理无人机上，远程推理仅在选择性调用时最为有效，因为每次调用都会引入延迟、资源成本、后端不确定性及对返回决策的验证需求。本文提出持久任务运行时（PMR）——一种无人机恢复框架，该框架保持任务循环与安全关键执行的本地化，仅将外部智能体推理器作为按需恢复模块使用。推理器从预定义恢复技能中选择决策，每个返回的决策在影响飞行前需经过解析、验证、安全过滤并映射至本地执行器动作。PMR引入了学习的调用认知价值（learned-CVI），这是一种紧凑的准入控制门，用于评估远程智能体推理是否可能提升近期任务进展以补偿其运行成本。在包含八个场景的固定400次Gazebo/PX4基准测试中，learned-CVI将硬/模糊场景成功率从纯本地自主的5.0%提升至95.0%，相比单次调用和周期性推理基线分别提高20.0和32.5个百分点，且相较于手动调参的规则式调用基线，远程智能体调用次数减少16.7%，日志令牌数减少29.2%。