Reconcile field measurements against the lab audit
A route that scores well in the lab and badly for real users, investigated until the gap is explained.
The ticket
Explain and narrow the gap between lab audit scores and real user measurements.
Acceptance criteria
- Both sources are reported side by side
- The cause of the divergence is identified
- At least one fix targeting the real-user measurement is landed
- The lab conditions are adjusted to better reflect real users
What lands as proof
Both datasets in one place with the divergence explained, which is what stops the lab score being trusted blindly.
Why teams defer it
- The lab score is the one that appears in a pull request, so it becomes the one people optimise.
- Field data needs enough traffic to be meaningful, which not every route has.
Questions
- What does the agent actually change?
- The ticket is scoped to one outcome: explain and narrow the gap between lab audit scores and real user measurements. Work that serves that outcome is in scope, and anything outside it is left for a separate ticket, so the pull request stays reviewable.
- How do I know the work is done?
- The pull request carries the evidence, not only the diff. Here that means both sources reported side by side in the pull request, so a reviewer can confirm the result without reproducing the work locally.
- How much oversight does this need?
- The run stops once the plan is written. Somebody reads the approach and approves it before any code exists, which is the cheapest moment to redirect the work.