Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos 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. I NTRODUCTION Large Language Models (LLMs) such as Bing [1], Gemini [2], and ChatGPT [3] have transformed natural language processing (NLP) through innovations such as transformer architectures [4] and large-scale pretraining [5].