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The feedback flywheel reviews successful and failed agent sessions. Findings feed back into instructions, tools, and quality sensors to improve later runs.
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A feedback flywheel is a recurring loop in which usage signals are collected, interpreted, and translated into focused product improvements.
The approach comes from combining product development, continuous learning, and feedback systems. Thoughtworks describes Feedback Flywheel as a technique for agentic systems in which feedback and observations feed the next improvement cycle.
Usage creates signals, signals become insights, insights trigger a change, and the change creates new usage signals. The loop needs visible feedback so that observations actually change behavior.
Usage data and feedback show where a product should change.
Observations become testable changes.
The change shapes the next round of use.
This pattern helps turn product feedback into a verifiable learning loop. It fits products with recurring use; many signals still need prioritization and can amplify noise.
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