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surveyChapter 1arXiv · 2023

A Survey on Large Language Model based Autonomous Agents

Lei Wang (Renmin University), Chen Ma (Renmin University)

Abstract

We present a comprehensive survey of LLM-based autonomous agents, covering construction, application, and evaluation. We analyze 150+ papers to identify key trends and open challenges.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

This survey is the definitive map of the LLM agent landscape as of 2023. For Zone III practitioners, it provides a structured overview of what has been tried, what works, and where the gaps are.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

  • →Comprehensive agent survey covering 150+ papers
  • →Agent construction taxonomy
  • →Open challenge identification

Topics

surveyautonomous agentsLLM agentsagent construction