AI-Assisted Engineering: Measure Outcomes, Not Activity
SMRTR summary
Measuring AI-assisted engineering by token usage or accepted completions tells you activity happened, not whether it helped. Platform teams should instead track outcomes by workflow — Terraform upgrades, test generation, PR reviews — connecting token costs to accepted results, review burden, and guardrail failures. Define success per workflow before measuring agents, and build dashboards that drive real platform improvements.
SMRTR provides this summary for quick context. The original article belongs to Daily.dev.
Read the original article