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How do I measure the human workload created by AI coding agents?

Short answer

Measure what the agents ask of the people directing them: time spent supervising rather than hands-on, how often agents interrupt for permission or input, how much switching between parallel sessions, and how much work moves after hours. Pair that with what engineers say about how the work felt, because telemetry can't read that part.

Why it matters

AI agents reduce some work and create other work. Supervision, verification and interruption don't appear in sprint metrics or token reports, and they tend to concentrate on a few people. If they're invisible, adoption decisions get made on half the picture of what changed.

How Ascenda helps

Ascenda reports, for a cohort of ten or more:

  • the share of AI-assisted time spent creating, verifying and supervising
  • oversight load over twelve weeks
  • interventions, and agent waiting by kind
  • concurrency and switching
  • focus blocks: hours in uninterrupted blocks and the longest stretch
  • after-hours and weekend share, on each person's own clock
  • self-reported state from ten-second check-ins, as a direction against the cohort's own baseline

Read more about the topic in What is supervisory engineering work?.

Next step

Start with a 30-day Report

The Report works from your engineers' imported history for a cohort of ten or more, with no integration, and ends in a written deliverable. The 90-day Study is the next stage and runs live.