对于人工智能代理而言，哪个问题更大：上下文共享还是提示？
最近我一直在使用基于大型语言模型（LLM）的代理进行开发，但遇到了一些反复出现的挑战：
1. 提示设计 - 确保代理的行为符合预期，而不需要过长或脆弱的指令。
2. 上下文共享 - 在时间上或代理之间传递记忆、结果和状态，而不至于使系统过载。
3. 成本 - 随着规模的扩大，令牌的费用迅速增加。
我很好奇其他人认为这里的真正瓶颈是什么，以及有没有解决这些问题的技巧或窍门。你们是在围绕令牌限制、内存持久性、优化提示设计等方面进行优化吗？
我很想听听你们对此的看法，或者是否有我们都忽视的更聪明的方法。提前感谢！
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Been building with LLM-based agents lately and keep running into a few recurring challenges:<p>1/ Prompting - making sure agents behave how you want without overly long, fragile instructions<p>2/ Context sharing – passing memory, results, and state across time or between agents w/o flooding the system<p>3/ Cost – tokens get expensive fast, especially as things scale<p>Curious what others think is the real bottleneck here, and any tips/tricks for solving this. Are you optimizing around token limits, memory persistence, better prompt engineering?<p>Would love to hear how you’re thinking about this or if there’s a smarter approach we’re all missing. ty in advance!