mlsanityData to 7 Oct 2026

Levels of AI Agents: from Rules to Large Language Models

arXiv 2405.06643 ↗First posted 6 Mar 2024cs.CLcited by 6

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Abstract (arXiv)

AI agents are defined as artificial entities to perceive the environment, make decisions and take actions. Inspired by the 6 levels of autonomous driving by Society of Automotive Engineers, the AI agents are also categorized based on utilities and strongness, as the following levels: L0, no AI, with tools taking into account perception plus actions; L1, using rule-based AI; L2, making rule-based AI replaced by IL/RL-based AI, with additional reasoning & decision making; L3, applying LLM-based AI instead of IL/RL-based AI, additionally setting up memory & reflection; L4, based on L3, facilitating autonomous learning & generalization; L5, based on L4, appending personality of emotion and character and collaborative behavior with multi-agents.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 24 of its 33 references that are in the map. First posted on arXiv 6 Mar 2024.

Builds on

24 of 33 references.

References · 24

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12 more
  1. 2023Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectivescited by 253
  2. 2023Multimodal Foundation Models: From Specialists to General-Purpose Assistantscited by 247
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

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  3. 2025LLM-based Multi-Agent System for Intelligent Refactoring of Haskell Codecited by 3
  4. 2025Distributed Approach to Haskell Based Applications Refactoring with LLMs Based Multi-Agent Systemscited by 0
  5. 2025Human-Centered Human-AI Collaboration (HCHAC)cited by 0
  6. 2026OpenDiscoveryTrace: Process Traces for Evaluating AI Scientist Workflowscited by 0

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