Testing
Testing belongs inside AI-assisted delivery
Why teams should define tests before using AI to generate implementation work.
By JirakJ
4 min read
I do not read this as a tooling problem first. I read it as a sign that AI-generated implementation arrives faster than the team can validate it.
If the only proof is a demo, I would treat the project as unfinished. That is why the early work should be concrete enough that engineering teams accelerating development with AI can argue with it.
The uncomfortable question
If this workflow disappeared for a week, who would notice first? That person is usually closer to the truth than the AI roadmap is.
The current failure mode
AI-generated implementation arrives faster than the team can validate it. That is operational debt. AI may make it more visible, but it will not clean it up by itself.
The intervention
Write acceptance checks and regression cases before generation starts. Keep it narrow enough that the team can see whether it works within days, not quarters.
The artifact
The artifact I would want is a test plan and acceptance checklist. Without that, the project depends too much on memory and confidence.
Monday morning checklist
- • Write the non-goals. Most bad AI projects expand because nobody says what is out of scope.
- • Write down the artifact that would make the work reviewable: in this case, a test plan and acceptance checklist.
- • Decide who owns the next version if the first version works.
- • Mark the part of the workflow where human judgment must stay visible.
If this sounds familiar
Start with one workflow. FlowMason AI can map it, identify the right intervention, and define whether the next step should be a prototype, agent, documentation pipeline or delivery system.
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