From Prompt to Action: A Comprehensive Review of LLM Autonomous Agents

Abstract

Large Language Models (LLMs) have quickly pushed the frontiers of autonomous agents with advanced reasoning, natural language interaction, and tool chaining in complex worlds. With LLM-based agents arising in domains like digital assistants, autonomous robots, and mission planning, it is more critical than ever to have a deep understanding of their construction, strengths, and weaknesses—especially for safety-critical and adversarial domains like space systems. This article presents an overview of the most recent developments in autonomous agents built with LLMs. We categorize modern architectures, single-agent and multi-agent architectures, and their most prominent functional modules—perception, reasoning, planning, and action. We present new functionality facilitated by LLMs, including zero-shot generalization, dynamic tool use, and human-AI collaboration, and criticize their drawbacks in real-world use, e.g., hallucination, limited resources, and safety. Besides, we discuss future standards and metrics for LLM agents, including how to measure dependability and robustness in hostile environments. Lastly, we present open research challenges highlighting the necessity of stable, efficient, and robust LLM-based agents deployable in wireless, remote, and hostile environments. This survey aims to offer researchers and practitioners a brief overview of the status quo with LLM-based autonomous agents and inspire future work bridging current gaps between general-purpose language intelligence and domain-specific autonomous systems.

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