Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos Engineering
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915275258986496 |
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| author | Owotogbe, Joshua |
| author_facet | Owotogbe, Joshua |
| contents | This study explores the application of chaos engineering to enhance the robustness of Large Language Model-Based Multi-Agent Systems (LLM-MAS) in production-like environments under real-world conditions. LLM-MAS can potentially improve a wide range of tasks, from answering questions and generating content to automating customer support and improving decision-making processes. However, LLM-MAS in production or preproduction environments can be vulnerable to emergent errors or disruptions, such as hallucinations, agent failures, and agent communication failures. This study proposes a chaos engineering framework to proactively identify such vulnerabilities in LLM-MAS, assess and build resilience against them, and ensure reliable performance in critical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03096 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos Engineering Owotogbe, Joshua Multiagent Systems Artificial Intelligence Software Engineering This study explores the application of chaos engineering to enhance the robustness of Large Language Model-Based Multi-Agent Systems (LLM-MAS) in production-like environments under real-world conditions. LLM-MAS can potentially improve a wide range of tasks, from answering questions and generating content to automating customer support and improving decision-making processes. However, LLM-MAS in production or preproduction environments can be vulnerable to emergent errors or disruptions, such as hallucinations, agent failures, and agent communication failures. This study proposes a chaos engineering framework to proactively identify such vulnerabilities in LLM-MAS, assess and build resilience against them, and ensure reliable performance in critical applications. |
| title | Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos Engineering |
| topic | Multiagent Systems Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2505.03096 |