Agentic AI for Safety-critical Multi-drone Systems: Challenges and Opportunities

A new position paper from arXiv (2608.21444) argues that agentic AI for multi-drone systems in safety-critical missions like search and rescue and infrastructure monitoring must be treated as a socio-technical design problem, integrating human oversight and evaluation as much as algorithms. The paper synthesizes lessons from two ongoing efforts, NAMUR and PERSIST, and calls for a human-centered, participatory research approach to build trust and governability.

arXiv:2608.21444v1 Announce Type: new Abstract: Multi-drone systems are increasingly positioned for safety-critical missions such as search and rescue SAR and critical infrastructure monitoring. Yet, real-world adoption remains constrained not only by autonomy performance, but by the difficulty of integrating agentic behavior into professional work: operators must understand, trust, and govern automation under uncertainty, time pressure, and accountability. This position paper synthesizes the ambitions and lessons from two ongoing efforts: NAMUR, which explores LLM-supported robot control in SAR and firefighting contexts, and PERSIST, which explores persistent drone operations for monitoring and security at critical infrastructure sites. We argue that agentic AI should be approached as a socio-technical design problem, where interfaces, oversight mechanisms, and evaluation practices are as critical as algorithms. We outline a human-centered, participatory, and iterative research approach aimed at uncovering stakeholder needs, shaping agent capabilities through successive prototypes, and producing transferable proof-of-concept systems and evaluation strategies for other safety-critical contexts.