"As agents using artificial intelligence have wormed their way into the mainstream for everything from customer service to fixing software code, it's increasingly important to determine which are the best for a given application, and the criteria to consider when selecting an agent besides its functionality. And that's where benchmarking comes in. Benchmarks don't reflect real-world applications However, a new research paper, AI Agents"
""The North Star of this field is to build assistants like Siri or Alexa and get them to actually work - handle complex tasks, accurately interpret users' requests, and perform reliably," said a blog post about the paper by two of its authors, Sayash Kapoor and Arvind Narayanan. "But this is far from a reality, and even the research direction is fairly new." This, the paper said, makes it hard to distinguish genuine advances from hype."
Current agent evaluation and benchmarking methods contain shortcomings that encourage development of agents that do well on benchmarks but fail in real-world applications. Agentic behavior lies on a spectrum defined by environment complexity, goal structure, and user interaction, making single-metric evaluations inadequate. Benchmarks often omit realistic environments, long-horizon goals, robust user interaction, and reliability measures, producing misleading utility signals. Evaluation should prioritize real-world task performance, environment fidelity, diverse scenario coverage, safety and reliability metrics, transparent protocols, and incentives aligned with practical deployment and user needs.
Read at InfoWorld
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