Engineering Measurement
Published engineering metrics so far: velocity, quality, cost, CI/CD, survey data, agent effectiveness, and team performance.
Velocity
- Engineering Output What do Total Output, Output/week, Output/week/eng, and AI Output Share show in the Velocity tab, and how do they relate to other Velocity metrics?
- Output Breakdown How is engineering output split between AI-Assisted, Human-Only, and Agent Output, and how is that split trending over time?
- Quality & Work Mix How is engineering output distributed across Hard, Medium, and Easy PR difficulty tiers, and is AI coding changing that distribution?
- Team Level Breakdown How do we see each engineer's Total, Average, and weekly Output score in the Velocity tab?
Quality
- Quality Metric Tabs Which code quality metrics are available in the Larridin Quality tab, and what do each of the sixteen metric views cover?
- Innovation Rate Chart How does the Innovation Rate chart in the Quality tab differ from the By Output and By PR views, and what does the category breakdown show?
- Team Level Breakdown How do we see each engineer's code quality fields at the individual level per week, including review behavior as both author and reviewer?
Cost
- Total Cost What do the Total Cost figure, trend %, Gross basis, and Actual/Observed toggle show in the Engineering Cost tab?
- Spend by Source Chart How is engineering AI spend distributed across sources week by week, and which platforms are driving the most spend?
- Team Level Breakdown How do we see each engineer's AI tool cost broken down by week and filterable by source in the Cost tab?
CI/CD
- Deployment Frequency What does the Deployment Frequency count show in the CI/CD tab, and how does it relate to the other CI/CD metrics?
- Change Failure Rate What percentage of deployments result in a production failure, and how is that rate trending as AI coding agents increase deployment frequency?
- Deploy MTTR How does Deploy MTTR measure recovery time from deployment failures in the CI/CD tab, and how does it relate to Change Failure Rate?
- Change Lead Time Where in the delivery pipeline is time accumulating, and how does Change Lead Time's stage breakdown show the specific constraint?
- CI Success Rate / Cancellation Rate / Median CI Duration How are CI Success Rate, Cancellation Rate, and Median CI Duration trending as AI coding tools increase pipeline volume?
- CI Run Outcomes Chart Which CI workflows are generating the most failures or cancellations, and when did those patterns emerge?
- Deploy Outcomes Chart How many production deployments are succeeding versus failing over time, and how does that volume relate to the Change Failure Rate?
- Deployment Log Where can we see a per-deployment record showing Status, Environment, Repository, SHA, Author, and Duration for every production deployment?
Survey Data
- Survey Summary What does the Survey summary card show for each engineering survey in the Survey Data tab?
- Survey Results What dimensions does the Survey Results table cover for each engineering survey respondent, and how do the per-respondent scores relate to ORG and TEAM averages?
Agent Effectiveness
- Agent Effectiveness Score How do we measure whether our engineers are using AI coding agents effectively, and where do we need to invest in training rather than better tools?
- Outcome Success What percentage of AI coding agent sessions in our engineering organization actually result in code that gets merged, and what's driving the sessions that don't?
- Cost per Outcome How do we calculate the true cost per unit of engineering output from AI coding agents, and how does that cost vary across the different tools our team uses?
- Session Depth How deeply are our engineers engaging with AI coding agents in each session, and what does session depth tell us about proficiency versus unproductive looping?
- Spend How does the amount we're billed for AI coding agents compare to what our telemetry shows we actually consumed, and what does the gap tell us about our agent cost governance?
- App Adoption How do we compare the cost-efficiency of every AI coding agent we're running, across adoption, sessions, spend, and outcomes, in a single view?
- Model Spend Breakdown Which foundation models are our AI coding agents calling, how much is each one costing, and are we routing to expensive frontier models for tasks that cheaper models handle equally well?
- AI Fluency Score Summary What specific engineering behaviors and infrastructure elements make up our AI Fluency Score, and which ones should we invest in developing to improve agent effectiveness?
- Team Breakdown How do we see each engineer's AI coding agent proficiency and cost efficiency side by side, so we can identify who to learn from and who needs development support?
Team Performance
- Output per Engineer How do we measure whether AI coding tools are actually increasing the engineering output our team produces, rather than just increasing the number of commits or PRs?
- Output by Category What proportion of our engineering team's AI-assisted output is going toward new features versus maintenance work, and is AI investment changing that mix over time?
- AI Output Share / AI PR Share Is AI coding producing a proportionate share of our engineering team's most complex and valuable work, or is it handling the simpler tasks while humans carry the heavier PRs?
- Prompt Quality How good are our engineers at prompting AI coding tools, and how much productivity are we leaving on the table because of gaps in prompting skill rather than gaps in tool capability?
- Cost per Output Is our AI coding tool investment making our engineering team more cost-efficient over time, or are costs rising as fast as, or faster than, the productivity gains?