Adoption & Agentic

Last updated: September 2026

This family answers a single question: how much is your team actually using AI, and how deeply?

It’s the most-read group of metrics in the dashboard because it surfaces the rollout story — are people picking up the tools, are they integrating them, and where are the gaps?

Agent Adoption Score
Formula, calculation, interpretation, and FAQ for Agent Adoption Score
Agent Autonomy Score
Formula, calculation, interpretation, and FAQ for Agent Autonomy Score
AI Tier
Formula, calculation, interpretation, and FAQ for AI Tier
Maturity Factor
Configuration, interpretation, and FAQ for the Maturity Factor setting
Cursor Boost
Configuration, interpretation, and FAQ for the Cursor Boost rate

The metrics

Metric What it captures
Agent Adoption Score 0–100 per developer. Combines daily consistency, hourly spread, prompt volume, and token output across Claude Code and Codex, with optional Cursor boost. The headline number on /ai-adoption/developers.
Agent Autonomy Score 0–100 per developer. Measures depth of autonomous AI use — sessions with 10+ tool calls across Claude Code and Codex (Cursor not included). Separates “asking AI a question” from “letting AI do real work.”
AI Tier Power User / Regular / Explorer / Emerging / On PTO. The composite category. Computed by weighting Adoption, Agentic, and Output Norm together (defaults 0.5 / 0.2 / 0.3).
Maturity Factor 0.01–1.00 org-wide ceiling knob (default 0.75). Adjusts all P90 scores so the tier ceiling fits your org’s actual maturity.
Cursor Boost 25% by default. How much Cursor adoption layers on top of Claude / Codex in the headline Adoption Score.

How the metrics fit together

Adoption Score is built from per-provider scoring (Claude, Codex, Cursor) using a four-factor blend:

  1. Daily Use — consistency across the window
  2. Hourly Spread — diversity within days
  3. Prompts — volume of interactions
  4. Output — token production depth

Claude Code and Codex events are unioned at the factor level (days, hours, prompts, tokens merged together — not averaged) and scored as one primary score. Cursor is scored independently and added as a secondary boost (default 25% of the Cursor score).

Then everything scales by Maturity Factor, which is your org-wide ceiling knob.

Code
Primary score   = blend(Claude ∪ Codex)
Final headline  = min(Primary + 0.25 × Cursor, 100) × Maturity Factor

Agentic is a separate, parallel measurement that runs against Claude Code and Codex sessions only — Cursor doesn’t currently feed Autonomy because Cursor’s event stream doesn’t expose per-session tool calls in a way we can score. Agentic does not roll into Adoption. Both feed into the AI Tier composite alongside Output Norm.

Tier composite (default; org-configurable)

Code
tierScore = (0.5 × Agent Adoption) + (0.2 × Agent Autonomy) + (0.3 × Output Norm)

Each weight is read from analytics.app_settings per organization (keys: tier_weight_adoption, tier_weight_agentic, tier_weight_output) and renormalized to sum to 1.0. The tier-badge tooltip in the app renders the live values for every developer so the breakdown is always transparent.

What admins can change in Settings → General: Maturity Factor, Developer Hourly Rate, Baseline Period, and Default Department, plus the tier composite weights (tier_weight_*) and the Output Score sub-weights (direct_commit_weight, review_weight, output_score_exclude_chore). All are per-org settings stored in app_settings and editable directly in the settings form.

Read this family responsibly

Three things to remember:

  1. The Adoption Score is about behavior, not skill. A developer can be brilliant and Emerging (their work this quarter doesn’t suit AI). The score is descriptive, not evaluative.
  2. Org P90 is the ceiling, not 100. Because we normalize against the org’s 90th percentile, a single developer can’t game the system by spamming prompts. The ceiling moves with the org.
  3. Maturity Factor sets the headroom. At 0.75, even a perfect (P90) developer scores 75. That’s intentional — it leaves room for the org to grow into higher numbers without retiering everyone overnight.

See How to think about developer scores before drawing individual conclusions.

Where this family shows up

  • /ai-adoption/developers — primary surface. Each row has Adoption, Agentic, Tier, and a heatmap.
  • /ai-adoption/teams — team averages and tier mix bars.
  • /ai-adoption/ai-tools-comparison — cohort comparisons (e.g. team A vs. team B, or Claude vs. Codex users). Devin is its own cohort here, scored on adoption and autonomy alongside Copilot, Cursor, and Claude rather than blended into another tool’s cohort.
  • /ai-adoption/executive — hero KPI (“AI Adoption %”) and trend lines.
  • /ai-adoption/ai-impact — autonomy deep dive and Business Impact / ROI.
  • Adjacent surfaces in the same nav family: /ai-adoption/board-metrics, /ai-adoption/capex, /ai-adoption/data-connections, /ai-adoption/data-explorer, /ai-adoption/settings/*.