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AI agents are beginning to transform industries from customer service to manufacturing, but understanding and improving agent decision-making remains a major challenge.
Agents often operate as black boxes, making tool selections — i.e., choosing which APIs, knowledge bases, or even other models to use — without clear reasoning. Traditional debugging methods fall short because we can’t fully decode these choices. Instead, we need to expose agent behavior through structured evaluations, using data-driven diagnostics to assess performance and refine decision-making.
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