A team should collect feedback through a repeatable loop: run the add-on with agreed inputs, capture the output, obtain a focused review, and keep checking behavior after deployment. A concrete check in the cited material is to enter values for the input variables that would be used in a flow and then select Run flow. The cited production-monitoring framework separately states that the functionality and behavior of the AI system and its components, as identified in the map function, are monitored when in production.
Make each test reproducible
For every run, the team can record:
- the input values;
- the output;
- the task requirement or acceptance criterion being checked;
- the reviewer’s observations; and
- the follow-up status.
Keeping the input, output, and assessment together gives reviewers a concrete basis for comparison. A complete feedback form, reviewer role, and review interval are not specified in the cited material, so those choices need to be made deliberately.
Ask for output-specific feedback
After a run, a short review can ask:
- Which part of the output was useful?
- Which part was unclear, missing, or unsuitable for the task?
- What in the output supports that assessment?
- What should be tested in the next run?
These are suggested prompts, not requirements from the cited sources. Linking each response to the exact input and output can make it easier to distinguish an isolated comment from a recurring pattern while keeping the discussion grounded in observable results.
Monitor after deployment
An initial test is not the end of feedback collection. The cited production-monitoring principle is relevant after deployment: the team can keep recording observed behavior instead of treating the first run as the final check. The source does not prescribe a survey format, schedule, or escalation rule; those choices need to be defined for the team’s own use.
What the team must still confirm
Before adopting the process, the team should document:
- what the add-on is expected to do;
- what makes an output acceptable;
- which inputs and use conditions matter;
- who reviews outputs and where feedback is recorded;
- when production monitoring takes place; and
- what happens when an output is unclear or unacceptable.
The cited sources provide a concrete testing step and a production-monitoring principle. They do not set the team’s task criteria, ownership, decision rules, or review schedule. A successful test can support a decision, but it does not guarantee future performance.