business-outcomes

作者质疑当前用任务完成率来衡量AI agent表现的做法，指出即使agent成功关闭了大量工单，也可能因未真正解决问题而导致客户体验下降。文章探讨应如何转向衡量下游业务结果、修正率和客户满意度等更实际的指标。

Salesforce discusses 'loop engineering' as a method for AI agents to evaluate their own progress, but argues that current metrics often fail to measure true business outcomes, suggesting agents should be aligned with shared business goals.

MIT researchers tracked 300 real AI implementations and found that only 5% of evaluations lead to full production deployment, with 95% of AI investment not producing measurable outcomes. Successful deployments focused on bounded tasks with defined success metrics.

A discussion on how to handle skeptical enterprise clients when selling AI agents, with advice to focus on business outcomes rather than the underlying technology.