A Large-Scale Empirical Study of Quality Assurance Practices and Gaps in AI Agents
A large-scale empirical study of quality assurance (QA) practices in 157 open-source LLM-based agent projects with at least 100 GitHub stars highlights the need to move beyond feature-level testing toward systematic end-to-end validation that ensures agent workflows remain within intended boundaries when interacting with untrusted inputs, tools, persistent state, and external APIs.
Wu-Yang Dai, Moses Openja, Jiho Shin et al.
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