# Agentic UAVs: LLM-Driven Autonomy with Integrated Tool-Calling and Cognitive Reasoning

arXiv: https://arxiv.org/abs/2509.13352 · HTML: https://mlsanity.com/paper/2509.13352/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 14 Sep 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)
- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 8 of 8)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility](https://mlsanity.com/paper/2501.02341/) (2025)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [REAL: Resilience and Adaptation using Large Language Models on Autonomous Aerial Robots](https://mlsanity.com/paper/2311.01403/) (2023)
- [UAV-VLN: End-to-End Vision Language guided Navigation for UAVs](https://mlsanity.com/paper/2504.21432/) (2025)
- [General-Purpose Aerial Intelligent Agents Empowered by Large Language Models](https://mlsanity.com/paper/2503.08302/) (2025)
- [UAV-CodeAgents: Scalable UAV Mission Planning via Multi-Agent ReAct and Vision-Language Reasoning](https://mlsanity.com/paper/2505.07236/) (2025)
- [Aero-LLM: A Distributed Framework for Secure UAV Communication and Intelligent Decision-Making](https://mlsanity.com/paper/2502.05220/) (2025)

## Built on by (most cited first, 7 of 7)

- [Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems](https://mlsanity.com/paper/2601.01891/) (2026)
- [Self in Space: Benchmarking Self-Awareness and Spatial Cognition in UAV Embodied Intelligence](https://mlsanity.com/paper/2607.12477/) (2026)
- [An LLM-Agnostic, MAVLink-Based Drone Command and Control Interface and Agentic Harness Using the Model Context Protocol](https://mlsanity.com/paper/2601.15486/) (2026)
- [Synthesizing the Kill Chain: A Zero-Shot Framework for Target Verification and Tactical Reasoning on the Edge](https://mlsanity.com/paper/2602.13324/) (2026)
- [Selective Agentic Recovery for UAV Autonomy with a Persistent Mission Runtime](https://mlsanity.com/paper/2606.14219/) (2026)
- [LLM-Centric Agentic AI for UAV Swarms: Architecture, Enabling Technologies, and Open Problems](https://mlsanity.com/paper/2607.09756/) (2026)
- [AURORA: A Natural Language-Driven Agentic Framework for Understanding, Reasoning, and Orchestrating Reliable Air-Ground Co-Simulation](https://mlsanity.com/paper/2609.19527/) (2026)

## Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs) are increasingly used in defense, surveillance, and disaster response, yet most systems still operate at SAE Level 2 to 3 autonomy. Their dependence on rule-based control and narrow AI limits adaptability in dynamic and uncertain missions. Current UAV architectures lack context-aware reasoning, autonomous decision-making, and integration with external systems. Importantly, none make use of Large Language Model (LLM) agents with tool-calling for real-time knowledge access. This paper introduces the Agentic UAVs framework, a five-layer architecture consisting of Perception, Reasoning, Action, Integration, and Learning. The framework enhances UAV autonomy through LLM-driven reasoning, database querying, and interaction with third-party systems. A prototype built with ROS 2 and Gazebo combines YOLOv11 for object detection with GPT-4 for reasoning and a locally deployed Gemma 3 model. In simulated search-and-rescue scenarios, agentic UAVs achieved higher detection confidence (0.79 compared to 0.72), improved person detection rates (91% compared to 75%), and a major increase in correct action recommendations (92% compared to 4.5%). These results show that modest computational overhead can enable significantly higher levels of autonomy and system-level integration.