I wanted to start a broader creative experiments thread around AI Agents.
A useful angle is troubleshooting, but it also connects with real-world use. Different tools and workflows can produce very different results even when the goal is similar.
It would be interesting to compare practical examples, experiments, setups, or lessons that show where AI Agents is genuinely useful and where it still needs improvement.
For Exploring AI Agents: creative experiments, I would separate the problem into setup, output quality and repeatability. agents and experiments fits into that because it can change the result quite a lot depending on the workflow. It is usually easier to troubleshoot when each change is tested on its own.
The point about agents and experiments is interesting. In practice I would keep the first test simple, record what worked, and only add extra complexity if it solves a specific limitation. That way you can improve the workflow without losing track of what caused the improvement.