如果你曾使用过大语言模型代理，一定感受过这种困扰：同一个模型在一种框架下显得极为出色，而在另一框架中却毫无用处。一个代理可能在工具使用排行榜上表现优异，但在一项简单的实际任务中却惨败。基准性能与实际可用性之间的差距，是该领域面临的最大挑战之一。在设计M2时，我们深知必须直面这一问题。这促使我们确立了两个核心目标，它们有时甚至相互冲突：

If you've worked with LLM Agents, you've felt this pain: the same model can feel brilliant in one framework and useless in another. An agent might crush a tool-use leaderboard but fail spectacularly at a simple, real-world task. This gap between benchmark performance and practical usability is one of the biggest challenges in the field. When we designed M2, we knew we had to tackle this problem head-on. This led us to two core, and sometimes conflicting, objectives: