Your agents should learn for the organization

This article discusses the challenge of AI agents learning from their interactions within an organization. It highlights that agents often discover outdated or incorrect guidance, but this learning is typically lost after the conversation. The author proposes that agents should capture potential learnings generously, then verify them through a review process before integrating them into organizational knowledge. This approach mirrors code review, ensuring high standards for accepted knowledge while allowing low-cost proposals. The article emphasizes the importance of treating human corrections and agent discoveries as signals that warrant investigation, not immediate truth.