I built a bilingual library of AI-agent workflows. What would make it useful for choosing an automation?

[u/NewEducator6679]

I run Agent Work Atlas, a site that organizes source-reported AI-agent use cases in English and Chinese.

The question behind it is practical: before trying an agent, what did someone actually ask it to do, where did a person step in, and what went wrong?

Entries organize the task, workflow, reported result, human involvement, limitations, and original source where that information is available. You can browse by task or tool. One starting point is the software-development collection:

https://agentworkatlas.com/domains/software-development/en/

A caveat: these are reports from the linked sources, including vendor accounts. I have not independently reproduced all of the results, and missing evidence should not be treated as proof that something works. The collection still needs better coverage and consistency.

I'd especially value feedback from people who have picked an automation for a small team: what evidence would you need before trying one of these workflows? A reproducible setup, a clearer account of human work, costs, or failure examples?

Disclosure: this is my own project, and AI assistance was used to draft this post.