The problem
Product teams need useful feedback before they invest in the next release. Testing takes time and effort, and slow feedback can delay the decisions that move a product forward.
The approach
Imagine a platform that collects application data and gives that context to AI agents. The agents interact with the application in ways similar to users, while the platform collects feedback for the team to review.
- Application dataGather the context the agents need to understand the application.
- Agent testingUse that context to explore interactions and exercise the application.
- FeedbackCollect observations that help inform product decisions.
What this enables
In my work with the platform, I have seen faster feedback, lower testing costs, earlier market entry and quicker scaling. These are qualitative observations from the project; I have not published comparative measurements.
The wider lesson
The useful starting point is the feedback a team needs. Choosing the technology comes after understanding that need. Context, a clear task and a way to review the output are central to putting agents to work.