See how your team
actually ships with AI.
Every engineer tries agents differently. Nobody knows what is sticking.
ax turns local coding-agent sessions into evidence for your AI enablement work: which workflows improve real shipping, where the team is stuck, and what should become standard practice.
The first AI rollout does not make a team AI-native.
Copilot, Cursor, Claude Code, ChatGPT — the tools arrive before the operating model. Some engineers build real agentic workflows; others stay at autocomplete. Leadership gets demos, not a shared way of shipping.
Tool chaos
Every engineer has a different stack, habit and prompt folder. ax shows the patterns under the sprawl.
No shared playbook
The useful workflows stay private until someone turns them into team practice. ax finds what is ready to teach or package.
Shipping feels the same
If cycle time isn’t moving, AI is still a side experiment. ax ties agent usage to the work that ships.
The evidence layer for internal AI enablement.
Your AI lead shouldn’t have to guess what stuck after the workshop. ax shows where leverage is showing up, where usage stays shallow, and which workflows are ready to spread.
Shared agent practice · 12 engineers
Aggregates only — not to police anyone, just to see whether AI is becoming part of how the team ships.
A faster team has a different shape.
Gates open as a workflow goes from one person’s trick to team practice. The scroll is your adoption curve.
Stuck gates are workflows still trapped in one head — ax finds them.
illustrative — your numbers render from real sessions
The collector is open source, so you can read exactly what leaves.
ax runs on each laptop and computes small derived rows. Only those aggregates ship; the sensitive work stays put by construction. Because it’s OSS, you can verify that line instead of trusting it.
what leaves the laptop
- Per-seat adoption signal (active days, depth of use)
- Skill / workflow usage rollups (names, not contents)
- What correlates with shipping (the patterns worth spreading)
- Team-level aggregates (never per-person behavior)
what never leaves
- Transcript text & prompts (read locally, never sent)
- Your code, diffs and file contents (stay on disk)
- What each person is building (yours to keep)
- Everything else ax touches to compute the rollups
What gets measured gets improved.
Right now AI adoption isn’t measured at all. A walkthrough on your own data shows what stuck, what stayed shallow, and which workflows are ready to become standard practice.