The hardest part of using AI agents isn’t building the agent
热度趋势
百分比基于当前可用热度信号，而非评论数或独立用户人数。
开发者社区讨论指出，使用AI代理最困难的部分并非构建代理本身，而是为其提供足够的上下文信息以执行有用的任务。用户普遍反映，尽管演示效果看起来很棒，但在实际应用中，代理往往因缺乏充足的上下文而难以发挥作用。社区成员正在探讨解决方案，包括使用n8n、Python等工具构建自己的代理，或者寻找能够更好地处理工作流程的现有平台。
It’s giving it enough context to actually do something useful. I’ve tried a few different agent/workflow setups recently, and I keep running into the same problem. The demo looks great:
Eg: Research these companies, compare them, summarize the findings and make a report.
But once you actually use it, you end up babysitting the thing:
explaining what sources to use
fixing the research direction
telling it what the output should look like
copying information between different tools
checking whether it actually finished everything
At that point I’m not sure if I’m using an agent or just supervising a very enthusiastic intern. What I actually want is something closer to:
Here’s the goal; figure out the steps; do the research; use the tools; organize everything; give me something I can actually use.
I’m curious what people here are actually using for this kind of workflow. your own agents with n8n / Python / MCP etc., or are there platforms that already handle more of the workflow for you?