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Fluency
A score per competency, per person. Not vibes — built from a static catalogue of 11 chat-observable indicators applied to every Enterprise-pathway session in a rolling 30-day window.
This is the 4D fluency framework — four habits that show up in real usage. In the app each habit carries a plain-language name.
| Competency | In-app habit | What it measures |
|---|---|---|
| Description | Clear prompts | How clearly the prompt frames the task — context, constraints, acceptance criteria |
| Delegation | Right task to AI | How well the engineer hands off versus over-specifying or babysitting the model |
| Discernment | Checks the output | How critically the engineer evaluates the response before accepting it |
| Diligence | Verifies before shipping | How reliably the engineer tests or cites the load-bearing claims before shipping |
Where fluency surfaces
There is no standalone org fluency page. Scores surface in two places:
My AI → Overview — each person's own view. A habit score per competency, a plain-English verdict ("You work with AI fluently / well / reasonably / cautiously"), and a tip against the weakest habit. This is the personal, self-service surface at /me/ai-agents.
Agent insights drill-downs — a person's fluency rollup appears on their detail drawer when you drill into them from Insights → Agent insights. This is what an engineering manager opens before a 1:1 to spot whether someone's delegation has shifted.
How a session is scored
Every Enterprise-pathway session that gets ingested is run through the classifier. The classifier produces:
- A boolean per indicator — present or absent in this conversation.
- A summary block with rationale (used in the per-session drilldown).
Org-level prevalence is then presentCount / conversationCount per indicator. Competency scores are aggregated across their indicators with a per-indicator weight from the catalogue.
Limits of the model
Worth being honest:
- Telemetry pathway orgs see no Fluency data — no prompt body, nothing to classify. Pick the Enterprise pathway if you want this.
- The classifier evaluates patterns in observable chat. It doesn't know whether the resulting code shipped, passed review, or caused an incident.
- Indicators are catalogue items, not custom prompts. We'd rather give you a stable benchmark than let every org drift its own.
Coding sessions only, today. The 4D framework and its indicator catalogue are calibrated against coding conversations — the indicators look for things like "specifies acceptance criteria" or "asks a clarifying question after a vague response," which read clean against an engineering session and noisier against, say, a marketing-copy or image-prompt session. Modality-aware scoring for image, video, audio and chat is on the roadmap. Until then, Fluency is silent on non-coding traffic — we'd rather show no score than a misleading one. See Catalog for what coverage your org has across modalities today.
What you do from here
- Spot training opportunities. A team with low Description prevalence usually benefits from a prompt-quality workshop.
- Catch model regressions. A sudden drop in Discernment across the org often correlates with a model release that confidently hallucinates more than its predecessor.
- Calibrate hiring. Compare new joiners' first-30-day Fluency to the org baseline.
Empty state
If no Enterprise-pathway sessions have been captured yet, the fluency tiles show "not enough analysed sessions yet". Enable the Enterprise pathway in Settings → AI → Agent Policy.