AI Engineering Economics in a Minute
Coding Agent ROI
A disciplined way to talk about coding-agent return on investment without false precision.
In one sentence
Coding-agent ROI compares valued outcomes from agent-assisted work to the full cost of producing those outcomes—including tokens, tools, and human review.
Why it matters
Vendors and enthusiasts quote huge productivity gains. Finance and engineering need explicit assumptions, comparable baselines, and refusal to treat token burn as value.
How it works
- Pick outcomes you can observe: accepted changes, cycle time, escaped defects, lead time—not vibes.
- Measure a baseline without the agent (or with a different workflow) on similar work.
- Sum costs: models, seats, infra, review time, incident time from agent mistakes.
- Compute return only on the outcome delta you can defend.
- Publish uncertainty; do not invent precision the data lack.
Example
If agent-assisted work cut median lead time on a known task class by 30% and added $2k/month cost with no measured defect increase, you have a directional ROI case. If you only know “we shipped more commits,” you do not yet have ROI.
What this proves
A careful ROI analysis proves a relationship between measured costs and measured outcome deltas under stated assumptions.
What this does not prove
A single team anecdote does not prove company-wide ROI. Token spend, commit counts, or lines of code alone do not prove positive return.
Last reviewed 2026-09-06. Title for citation: AI Engineering Economics in a Minute: Coding Agent ROI.