Select interventions for interrupted AI completion
Interrupted AI interactions often leave teams unsure what outcome actually failed.
Continuity, clarity, and compute efficiency can point toward different definitions of success.
“When someone switches tabs, I can’t tell whether we saved their work or just burned tokens.”
Valentina Kozlov · Product Engineer
Builds a streaming AI workflow inside a customer-facing web application with multi-step user interactions.
What pulls against what
- user continuity vs. desired cancellation
- completion rate vs. compute efficiency
- trace evidence vs. user intent
- fast default behavior vs. validated outcome
What is at stake
The problem is visible, but the right outcome is not. A well-designed experiment can reveal whether continuity, cancellation, or clearer progress creates the best user result
Why Vercel
At Vercel, this can matter as AI application behavior becomes part of the web experience teams operate.
Written for
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.