How do I measure the ROI of AI agents for my team?

Not with lines of code or tokens, which flatter and mislead. Measure ROI on outcomes: throughput on real work (features shipped, tickets closed at your quality bar), the review and rework agents create, and cost per task. The honest number is net of the time seniors spend steering and fixing. An agent that doubles output but triples review hasn't paid off, and you see it only if you measure the whole loop, not the demo.

0106the metrics that lie

The metrics that lie

Lines of code, suggestions accepted, tokens generated — all easy to move and none tied to value. They make a dashboard look good while telling you nothing about whether real work got done better.

0206measure the whole loop

Measure the whole loop

Throughput on real work at your quality bar, minus the review and rework agents add, over the cost to run them. ROI is net of steering time, not gross output. That's the number that survives contact with a CFO.

0306attribute it per task

Attribute it per task

Instrument cost and outcome per run so you can say which work agents made cheaper or faster, instead of arguing from a monthly bill and a feeling.

0406common questions

Straight answers.

Isn't more code shipped a good sign?: Only if it's good code that met your bar with acceptable review. Volume without that is just faster mess; measure outcomes net of rework.
What's a fair ROI baseline?: Your own pre-agent throughput and cost on comparable work. Compare net output at the same quality bar, not against a vendor's benchmark.
How long to see real ROI?: After the operating model lands, not in week one. Early on you're paying the learning curve; measure the steady state, honestly.