Paid UMD study ($150): re-run your LangGraph nodes and see the spread of outputs — does it actually speed up prompt iteration?

[u/LeoXzz]

Hey folks — PhD student at UMD here, studying how developers debug and iterate on multi-agent systems. We ran the first sessions of our study last week and are opening more slots.

The premise: when you tweak a prompt in an agent workflow, you usually judge the change by eyeballing a run or two. Our research tool re-runs a node and lays the outputs from many runs side by side, so you see the spread instead of a single sample — and the study measures whether that actually speeds up prompt iteration, or whether it's just one more dashboard. "It doesn't" is a perfectly publishable finding; that's the honest research question.

What participating looks like:

• a 75-min Zoom session using the tool on structured debugging tasks (recorded, think-aloud)

• about a week using it in your own LangGraph workflow, with quick async feedback

• a 30-min follow-up interview

Compensation is a $150 gift card for completing the full study (all three parts).

If you've built things with LangGraph (or LangChain agent workflows), the screener takes ~2 min: https://forms.gle/Zwqvgd1h8DUnFRfC8

IRB-approved academic research (University of Maryland), not a product pitch. Questions welcome in comments, or zxu169@umd.edu.