大模型有一些结构性弱点，直接限制了智能体在真实业务中的应用价值，因此智能体工程的一大核心工作，就是在模型外围，用工程手段补齐短板、设置边界、约束行为。
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This started with Addition Under Pressure, where I gave Claude Code and Codex the same prompt: train the smallest possible transformer that can do 10-digit addition with at least 99% accuracy. Claude Code came back with 6,080 parameters and Codex came back with 1,644. The community has since pushed this dramatically lower.