Defining and Detecting the Defects of the Large Language Model-based Autonomous Agents
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| Publicat a: | arXiv.org (Dec 24, 2024), p. n/a |
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| Autor principal: | |
| Altres autors: | , , , , , , |
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Cornell University Library, arXiv.org
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| Accés en línia: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3149108561 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3149108561 | ||
| 045 | 0 | |b d20241224 | |
| 100 | 1 | |a Ning, Kaiwen | |
| 245 | 1 | |a Defining and Detecting the Defects of the Large Language Model-based Autonomous Agents | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 24, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a AI agents are systems capable of perceiving their environment, autonomously planning and executing tasks. Recent advancements in LLM have introduced a transformative paradigm for AI agents, enabling them to interact with external resources and tools through prompts. In such agents, the workflow integrates developer-written code, which manages framework construction and logic control, with LLM-generated natural language that enhances dynamic decision-making and interaction. However, discrepancies between developer-implemented logic and the dynamically generated content of LLMs in terms of behavior and expected outcomes can lead to defects, such as tool invocation failures and task execution errors. These issues introduce specific risks, leading to various defects in LLM-based AI Agents, such as service interruptions. Despite the importance of these issues, there is a lack of systematic work that focuses on analyzing LLM-based AI Agents to uncover defects in their code. In this paper, we present the first study focused on identifying and detecting defects in LLM Agents. We collected and analyzed 6,854 relevant posts from StackOverflow to define 8 types of agent defects. For each type, we provided detailed descriptions with an example. Then, we designed a static analysis tool, named Agentable, to detect the defects. Agentable leverages Code Property Graphs and LLMs to analyze Agent workflows by efficiently identifying specific code patterns and analyzing natural language descriptions. To evaluate Agentable, we constructed two datasets: AgentSet, consists of 84 real-world Agents, and AgentTest, which contains 78 Agents specifically designed to include various types of defects. Our results show that Agentable achieved an overall accuracy of 88.79% and a recall rate of 91.03%. Furthermore, our analysis reveals the 889 defects of the AgentSet, highlighting the prevalence of these defects. | |
| 653 | |a Descriptions | ||
| 653 | |a Static code analysis | ||
| 653 | |a Large language models | ||
| 653 | |a Defects | ||
| 653 | |a Workflow | ||
| 653 | |a Natural language | ||
| 700 | 1 | |a Chen, Jiachi | |
| 700 | 1 | |a Zhang, Jingwen | |
| 700 | 1 | |a Wei, Lia | |
| 700 | 1 | |a Wang, Zexu | |
| 700 | 1 | |a Feng, Yuming | |
| 700 | 1 | |a Zhang, Weizhe | |
| 700 | 1 | |a Zheng, Zibin | |
| 773 | 0 | |t arXiv.org |g (Dec 24, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3149108561/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.18371 |