这篇来自 Stanford 和 Harvard 的论文解释了为什么大多数“agentic AI”系统在演示中令人惊艳，但在实际使用中却完全崩溃。
核心论点简单且让人难以接受：agent 的失败并非因为缺乏智能。它们失败是因为无法适应。
研究表明，大多数 agent 被构建用于执行计划，而非修改计划。它们假设世界保持稳定，工具按预期运作，目标始终有效。一旦其中任何一点发生变化，agent 依然会一意孤行，自信地一次次做出错误的举动。
作者在执行与适应之间划出了明确的界限。
执行是遵循计划。
适应是察觉到计划有误并在推进过程中改变行为。
如今的大多数 agent 只做到了前者。
几个关键洞察尤为突出。
适应不是 fine-tuning。这些 agent 没有被重新训练。它们通过监控结果、识别失败模式，并在任务运行期间更新策略来实现适应。
僵化的工具使用是一种隐藏的失败模式。将工具视为固定选项的 agent 会陷入僵局。能够根据反馈对工具进行重新排序、放弃或切换的 agent，表现要出色得多。
记忆胜过单纯的推理。那些从过去的成功与失败中提取简短、结构化经验并存储起来的 agent，表现优于依赖长链推理的 agent。记住什么有效，比想得更深更重要。
结论十分直白。
规模化扩展 agentic AI 并不取决于更大的模型或更复杂的提示词。关键在于系统能否察觉现实与预设假设的偏离，并做出智能响应，而不是盲目地一往无前。
如今的大多数“autonomous agents”无法适应环境。 它们只是执行。
而没有适应能力的执行，不过是营销包装更精美的自动化罢了。
This paper from Stanford and Harvard explains why most “agentic AI” systems feel impressive in demos and then completely fall apart in real use.
The core argument is simple and uncomfortable: agents don’t fail because they lack intelligence. They fail because they don’t adapt.
The research shows that most agents are built to execute plans, not revise them. They assume the world stays stable. Tools work as expected. Goals remain valid. Once any of that changes, the agent keeps going anyway, confidently making the wrong move over and over.
The authors draw a clear line between execution and adaptation.
Execution is following a plan.
Adaptation is noticing the plan is wrong and changing behavior mid-flight.
Most agents today only do the first.
A few key insights stood out.
Adaptation is not fine-tuning. These agents are not retrained. They adapt by monitoring outcomes, recognizing failure patterns, and updating strategies while the task is still running.
Rigid tool use is a hidden failure mode. Agents that treat tools as fixed options get stuck. Agents that can re-rank, abandon, or switch tools based on feedback perform far better.
Memory beats raw reasoning. Agents that store short, structured lessons from past successes and failures outperform agents that rely on longer chains of reasoning. Remembering what worked matters more than thinking harder.
The takeaway is blunt.
Scaling agentic AI is not about larger models or more complex prompts. It’s about systems that can detect when reality diverges from their assumptions and respond intelligently instead of pushing forward blindly.
Most “autonomous agents” today don’t adapt. They execute.
And execution without adaptation is just automation with better marketing.