Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos Engineering

• URL: http://arxiv.org/abs/2505.03096v1
• Date: Tue, 06 May 2025 01:13:14 GMT
• Title: Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos Engineering
• Authors: Joshua Owotogbe,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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