Different metrics answer different questions
Adoption metrics show who has access and who uses a tool. Usage metrics show sessions, models, tokens, or tool calls. Workflow metrics show exploration, retries, clarification, and verification.
Cost metrics describe spend. Quality and outcome metrics connect the work to review, tests, rework, delivery, or other agreed evidence. Mixing these layers makes a dashboard look precise while hiding what it actually says.
Build a measurement vocabulary
For a pilot, write down the management question first: are teams using the agent for suitable work, is review burden changing, or why do similar tasks cost different amounts?
Then choose one or two signals from each relevant layer. A small, interpretable set is more useful than a large inventory of activity that nobody can act on.
Avoid proxy optimisation
When a metric becomes a target, people adapt to the number. Token counts can reward short conversations, lines of code can reward volume, and raw adoption can reward access without effective use.
Use metrics to investigate the system and support conversations. Keep task complexity, tool boundaries, and missing evidence visible.
Frequently asked questions
What is the most important AI coding metric?
There is no universal metric. The right measure depends on the work and the question the team is trying to answer.
Should token usage be on the dashboard?
Usually as cost or activity context, not as a standalone measure of productivity or quality.
TraceYield
Start with one real work trajectory.
Discuss the question you want to investigate with TraceYield and the context required to answer it.
Request a pilot