Scenario A: A Flywheel At Work
Imagine this.
It was a quiet Tuesday afternoon.
The team had built an internal decision-support system to assist customer support agents with ticket classification and routing.
The system had been running steadily for several weeks. Its role was narrow but important: classify inbound support requests, route them to the right team, and attach a short explanation so humans could act quickly and confidently.
The system was doing its job.
As part of a routine review, an operator noticed a small shift. Resolution times for one class of requests had begun to creep upward over the past few days. The change was subtle, but consistent enough to merit a closer look.
Every routing decision carried context: the input, the model’s confidence, and the eventual outcome. That information flowed into a lightweight daily review, treated as ordinary operating practice rather than an exception.
A small sample made the pattern clear.
Requests that combined billing changes with technical issues were routed correctly, but the explanations lacked clarity. Humans were resolving the cases successfully, yet only after rereading the original request and reconstructing intent themselves.
The system was reaching the right destination, but leaving too much work for the human on the other side.
This fell well within the system’s current scope. Still, it revealed something important: explanation quality mattered operationally, even when accuracy appeared strong.
The operator flagged the pattern and brought it into the next review.
The conversation stayed grounded. The question was not whether the model was “good,” but whether the system was reinforcing the behavior the team actually valued.
Explanation quality had been assumed to improve alongside routing accuracy. The data now showed that assumption clearly enough to examine it.
A small change followed. A new outcome category was added: correct route, weak explanation. Recent cases were tagged. The evaluation harness was updated to surface the signal explicitly.
The system continued running as usual.
Within days, the new signal appeared consistently in review. Sometimes it improved. Sometimes it plateaued. Each result added clarity about how the system behaved under real usage.
A week later, the team made a focused adjustment to the explanation prompt. It shipped behind a flag. The signal improved steadily.
Resolution times returned to baseline. Manual clarification declined. More importantly, explanation quality became visible early, while the cost of adjustment was still low.
From the outside, nothing changed.
Internally, the system had learned because the loop was intact:
- Actions produced signals.
- Signals informed evaluation.
- Evaluation shaped behavior.
The operator left a short note explaining what changed and why, written written for the next person who would inherit the system.
Two weeks later, another pattern surfaced and followed the same path.
The system grew easier to operate, easier to reason about, and incrementally more trustworthy.
That is what a working flywheel feels like.