VentureBeat July 6, 2024
AI agents are becoming a promising new research direction with potential applications in the real world. These agents use foundation models such as large language models (LLMs) and vision language models (VLMs) to take natural language instructions and pursue complex goals autonomously or semi-autonomously. AI agents can use various tools such as browsers, search engines and code compilers to verify their actions and reason about their goals.
However, a recent analysis by researchers at Princeton University has revealed several shortcomings in current agent benchmarks and evaluation practices that hinder their usefulness in real-world applications.
Their findings highlight that agent benchmarking comes with distinct challenges, and we can’t evaluate agents in the same way that we benchmark foundation models.
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