以简单的方式构建高效的人工智能代理
我读了一篇来自Anthropic的好文章，讨论了人们如何构建有效的AI代理。让我印象最深刻的一点是：保持简单。
最佳的设置不需要庞大的框架或花哨的工具。它们将任务分解为小步骤，进行充分测试，只有在必要时才添加更多内容。
我正在尝试遵循的几点：
1. 不要让它过于复杂。一个单一的LLM加上一些工具就能满足大多数情况。
2. 只有在真正有帮助的情况下，才使用像提示链或路由这样的工作流程。
3. 了解代码在后台的运行原理。
4. 花时间为代理设计良好的工具。
我正在通过构建小型代理项目来测试这些想法。很想听听大家是如何构建代理的！
Anthropic文章链接：https://www.anthropic.com/engineering/building-effective-agents
如果你感兴趣，我在这里分享这些代理：https://github.com/Arindam200/awesome-ai-apps
查看原文
I read a good post from Anthropic about how people build effective AI agents. The biggest thing I took away: keep it simple.<p>The best setups don’t use huge frameworks or fancy tools. They break tasks into small steps, test them well, and only add more stuff when needed.<p>A few things I’m trying to follow:<p>1. Don’t make it too complex. A single LLM with some tools works for most cases.<p>2. Use workflows like prompt chaining or routing only if they really help.<p>3. Know what the code is doing under the hood.<p>4. Spend time designing good tools for the agent.<p>I’m testing these ideas by building small agent projects. Would love to hear how you all build agents!<p>Link to Anthropic's Post: https://www.anthropic.com/engineering/building-effective-agents<p>If you’re curious, I’m sharing the agents here: https://github.com/Arindam200/awesome-ai-apps