How I Measure Whether My Agent Is Actually Saving Me Time
Description and use
A developer implemented a comprehensive measurement system for AI agent efficiency, considering human baseline, agent wall time, review, rework, and interruption counts, enabling objective assessment of true time savings.
Problem
Determining if an AI agent truly reduces overall task time, beyond surface metrics like agent wall time.
Outcome
Approximately 30% of 400 tasks became cheaper than manual execution, 20% were abandoned after five attempts, and the remaining tasks remained in a neutral zone until tooling improved.
Workflow
- 1. Measure human baseline by running the task manually three times and averaging the duration.
- 2. Run the task with the agent, recording agent wall time from start to accepted result.
- 3. Record review time spent checking the agent’s output.
- 4. Record rework time for any corrections needed due to incorrect results.
- 5. Calculate total agent effort as agent wall time + review time + rework time.
- 6. Compare total agent effort to human baseline; if lower, the task is worth automating.
- 7. Require at least five runs before making a decision to account for initial regressions.
- 8. Track interruptions where the agent pauses for human input, noting frequency and duration.
- 9. Batch approvals to reduce context switches.
- 10. After ten runs, if success rate does not improve, discontinue automation for that task.
Evidence
For each task I track four numbers: human baseline, agent wall time, review time, rework time.
Agent wall time is the number everyone quotes and the least interesting one. review + rework is where the truth lives.
My rule: a task is worth automating when agent wall time + review + rework < human baseline but only after at least 5 runs.
The metric I did not expect to matter: how many times the agent stopped and waited for me.
Roughly 400 tasks. About 30% ended up cheaper than doing them by hand, and those accounted for most of the saved time.
- Record date: 2026-10-20T23:41:07.500Z