Metrics glossary
This page defines every metric you encounter in CodeTogether AI, with what counts and what does not. Read the foundational terms and conventions first, then look up any metric by the surface it appears on.
- Deployment: SaaS, On-premises
Foundational terms
The following terms carry weight throughout:
| Term | What it means |
|---|---|
| Developer-day | One developer on one calendar day. Metrics expressed per developer-day count developer-days, so five developers active over four days is twenty developer-days |
| Line-equivalent | A common unit for kept work that puts code and non-code output on one comparable scale. Non-code content is converted using a fixed normalization, and totals are capped so converted work never exceeds the lines actually changed |
| Analytics data | A separate, purpose-built copy of your activity, reorganized for fast reporting. It is derived, so it can always be recomputed from the captured activity underneath |
| Rollups and facts | Captured activity is refined in stages: raw events become work segments, segments and commits become facts, and facts are pre-summed into rollups. Each stage is narrower than the last. These stage names appear as-is in developer traces |
| Retained work | Kept output: work still present at measurement time, not later removed or overwritten. Underpins retention, abandonment, and contribution-mix metrics |
| AI-active day | A day with captured AI or agent activity for the subject. Measured against the subject's active days, not the calendar window |
Two counting conventions
Two conventions apply throughout, and the glossary flags which one a metric uses:
- Effort versus elapsed. Some time metrics count effort, where overlapping sessions add up, and some count elapsed wall-clock time, where overlaps are de-duplicated.
- Additive versus distinct. Some counts are summed across days, so they grow with the window. Some are distinct counts, deduplicated across the window.
The zero-versus-blank convention
The product distinguishes three states, and reading them correctly is how you tell missing data from a real zero:
0means a measured zero.- A blank means not captured. The value is missing, not zero.
- An empty list means measured, none found.
Headline metrics
These metrics appear across several surfaces. The following table defines them:
| Metric | Plain-English definition | Counting rule |
|---|---|---|
| Total coding time | Captured hands-on development time | Effort, not elapsed: overlapping sessions both count, so one person's day can exceed 24 hours. Appears as Active time on Tooling |
| Commits | Code commits made in scope | Captured natively, no GitHub connection required. Breaks down by branch, repo, or developer |
| Branch active-days | Open-branch load over the period | A count of branch-days a branch was open, additive across days. 14 can be one branch across 14 days or 14 branches on one day. Not a count of distinct branches open right now |
| Sessions | Coding-tool runtime sessions captured | Additive. Splits into IDE sessions (in-editor) and Agent sessions (agent and MCP, Model Context Protocol, tools) |
| Agentic and in-editor | The work split. Agentic is work through an agent, CLI, or MCP tool. In-editor is human-directed work in an IDE, with or without inline AI | Drives many charts: agent sessions, agentic minutes, agentic adoption, and the human-versus-agentic mix |
| Stale work | Open work that has gone quiet, with no activity for 14 or more days | Assessed as of the end of the selected period, not as of today, so it can be trended over time. Not the same as abandoned work |
| Value points | The weighted output-quality currency, comparable across people, tools, and languages, and divisible by cost. As a rough scale, an average engineer using traditional development delivers one to two value points in a week — see Value points | Computed per branch. Where AI analysis is unavailable, labeled output points |
Metrics on Home and the Activity overview
The following table lists the overview metrics:
| Metric | Definition | Counting rule |
|---|---|---|
| Active developers | Distinct people with captured activity in the window | Distinct count, never summed across periods |
| Coding time | Captured hands-on developer minutes | See Total coding time (effort) |
| AI sessions | Runtime sessions captured | IDE plus agent sessions, additive |
| AI adoption | Share of active developers using any AI | Distinct developers with an AI-active day, divided by distinct active developers |
| Agentic spend | Dollar cost (USD) of agentic tool usage, across the team for the period | From captured token usage through agent, CLI, and MCP tools, priced against the platform's built-in rate card — list pricing, not your organization's negotiated vendor rates. Excludes in-editor AI usage |
| Agentic adoption | Share of active developers using agent workflows | Distinct developers with an agentic-active day, divided by active developers |
| Lines changed | Lines added plus deleted | Summed over the window |
| Retained output | Kept work, in line-equivalents | Capped line-equivalents of work that survived, across all work models |
| Commit-to-push lag | Average time from a commit to the push that carried it to the remote | Hours, averaged across commits observed pushed; a commit never observed pushed is excluded rather than counted as zero or infinite |
Metrics on the Tooling dashboard
The following table lists the tooling metrics:
| Metric | Definition | Counting rule |
|---|---|---|
| Total sessions | Runtime sessions captured | Additive |
| Total active minutes | Captured developer minutes | See Total coding time |
| Total prompts | Prompt events captured | Sum |
| Per-tool sessions, minutes, tokens, prompts | Usage per AI tool | Summed and grouped by tool |
| Average session duration by tool | Typical session length per tool | Elapsed wall-clock minutes, divided by sessions |
| In-editor by host | Usage by IDE (VS Code, Cursor, JetBrains, Eclipse, Visual Studio) | Editor-host sessions, minutes, and developers, excluding agent and MCP |
| Agentic versus in-editor time | The elapsed-time split | Agentic wall-clock versus IDE wall-clock minutes |
Metrics on the Developer profile
The following table lists metrics that appear only on the Developer profile:
| Metric | Definition | Counting rule |
|---|---|---|
| High-AI-usage days | Days above the AI-intensity threshold | A day counts when prompts or tool calls reach 10 in a day, or AI/agentic minutes reach 120 in a day |
| Completion rate | Share of the developer's tracked work items completed in the window | A proxy metric: completed work items divided by all tracked work items. Not an AI-suggestion-acceptance rate |
| Adoption depth | Share of active days that used AI | AI-active days divided by active days |
| Agent-driven sessions | Share of all captured sessions that were agentic rather than in-editor | Agent sessions divided by IDE sessions plus agent sessions. In-editor sessions count even when no AI was used |
Metrics on the AI adoption report
The following table lists the report metrics:
| Metric | Definition | Counting rule |
|---|---|---|
| AI adoption rate | Share of developers using any AI. The same measure as AI adoption on Home and the Activity overview — the report labels it as a rate | Distinct developers with any AI-active day, divided by active developers |
| Output efficiency (VP/$) | Value points per dollar of allocated engineering cost | Value points divided by total allocated engineering plus AI cost |
| Cost per value point | Dollars per value point produced | Allocated cost divided by value points |
| Acceleration factor | How many times more value per dollar agentic returns versus in-editor | Agentic VP/$ divided by in-editor VP/$ |
| Value per headcount | Value-weighted output per working head | Retained units divided by active developers |
| Budget utilization | Share of developers using 80% or more of their AI token budget | Distinct developers at 80% or more of quota, divided by active developers |
AI code and retention metrics
The following table lists the code-quality metrics:
| Metric | Definition | Counting rule |
|---|---|---|
| AI code retention | Share of AI-generated work that was kept | Retained AI line-equivalents divided by generated AI line-equivalents |
| AI abandonment rate | Share of AI work later removed | The complement of retention, per developer on the profile |
| Work-model contribution mix | Output share by agent, human-with-AI, and human-only | Retained-unit share per work model |
| Waste cost | Dollar value of discarded AI work | Session-level discarded-cost components |
Related
- Value points — what value points measure.
- Dashboards — where these metrics appear.
- AI adoption report — the report these adoption metrics drive.
- Validate data with developer traces — auditing any metric to its source.