The hardest part of using AI agents isn’t building the agent
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A developer community discussion highlights that the main challenge with AI agents isn't their construction, but providing sufficient context for them to perform useful tasks. Users often find that while demos appear promising, real-world application struggles with a lack of adequate contextual information. The community is seeking solutions, including custom agents built with tools like n8n or Python, or platforms that streamline the workflow.
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?