Explore/agent app/Agentic AI for Safety-critical Multi-drone Systems: Challenges and Opportunities
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Timothy Merritt, Alejandro Jarabo-Peñas, Juan Bravo-Arrabal, Maria-Theresa Bahodi, Anders Lyhne Christensen/Agentic AI for Safety-critical Multi-drone Systems: Challenges and OpportunitiesUnknown

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.

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55 / 100
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55Score
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40C
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30C
Documentation
70C+
Maturity
45C
Community
42C
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58C
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2026-08-262026-08-31
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✓ Best for

  • Research and experimentation
  • Prototype development
  • Learning agentic patterns

◎ Strengths

  • Active community
  • Open source
  • Well-documented API

✕ Not ideal for

  • Untested at scale without validation
  • Teams without AI/ML expertise

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AgentHub Score

55
Score 55/100
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