Measuring collaboration quality with coding agents

We’re seeing real productivity gains when using coding agents, but most evaluation metrics still focus too heavily on coding throughput, such as time to first output, lines of code generated and tasks completed. Measuring collaboration quality with coding agents helps teams avoid falling into "the speed trap" by shifting focus toward how effectively humans and agents work together. Metrics such as first-pass acceptance rate, iteration cycles per task, post-merge rework, failed builds and review burden provide more meaningful signals than speed alone. Teams using Claude Code can use the /insights command to generate reports reflecting on successes and challenges from agent sessions. Our teams have also experimented with tracking first-pass acceptance of a customized /review command.

In practice, shorter feedback cycles and fewer failed builds indicate more effective interaction with coding agents. When teams find themselves in repeated back-and-forth with their agents, these metrics highlight opportunities to improve the feedback flywheel. We recommend tracking collaboration quality at the team level, rather than the individual level, alongside DORA metrics to build a more complete picture of coding agent adoption.