Utility is all you need

Closing the Agent Learning Loop with Utility-Ranked Memory Your agent failed on the same edge case last Tuesday. Someone on the team noticed, tweaked the prompt, and redeployed. This Tuesday, it failed again. Different input, same root cause. The prompt fix was too narrow. The trace was sitting right there in your observability stack, but nothing connected that signal back to the agent's behavior. This is the default state of most production AI agent systems today. Teams have traces. They have evals. What they do not have is a mechanism that turns failure signals into better retrieval on the next run. We built Reflect to close that gap. Reflect is an outcome-informed memory layer that stores reflections from past runs, scores them by how useful they actually were, and re-ranks retrieval so your agent improves with every reviewed trace, not just every prompt rewrite. The missing layer between evals and action Production agent stacks have three components: observability ( traces), evalua