Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos Engineering
This addresses robustness problems for developers deploying LLM-MAS in real-world settings, but it is incremental as it applies an existing engineering approach to a new domain.
This study tackles the vulnerability of Large Language Model-Based Multi-Agent Systems (LLM-MAS) to emergent errors like hallucinations and communication failures in production environments by proposing a chaos engineering framework to proactively identify and build resilience against these issues, ensuring reliable performance in critical applications.
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.