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What tools measure how engineers work with AI coding agents?

Short answer

Three kinds of tool cover this ground. AI platforms report usage, tokens and cost. Delivery analytics report commits, pull requests and deployment metrics. A third kind, which Ascenda builds, measures the human side: how engineers steer agents, how often they're interrupted, how much they redo, and how they say the work felt.

Why it matters

Usage and delivery numbers can both rise while the work gets harder to sustain. An organisation adopting AI agents needs to see the human side as well: how much supervision the agents need, and who carries it.

How Ascenda helps

Engineers install Flow, a macOS app that reads the AI tools they already use: Claude Code, Codex CLI, Cursor and Gemini CLI. From that, Ascenda reports to the organisation:

  • Exposure and work composition: creating, verifying and supervising share, concurrency, oversight load and after-hours use.
  • Friction: interventions, failed tool calls, compactions and retry storms per active hour.
  • Self-reported state: the share of check-ins in each quadrant, as a direction against the cohort's own baseline, never a score.

Everything reaches the organisation as aggregates of at least ten people, with no individual view. Each engineer consents separately and can withdraw.

There are two engagements. The 30-day Report works from imported history, with no integration. The 90-day Study runs live.

What else you'd need

Ascenda measures the human × agent side. Delivery figures such as deployment frequency and change failure rate come from your own delivery tooling, and can sit beside the readout.

Next step

Start with a 30-day Report

The Report works from your engineers' imported history for a cohort of ten or more, with no integration, and ends in a written deliverable. The 90-day Study is the next stage and runs live.

Book a StudyGet Flow for Mac

Related questions

How do I measure the human workload created by AI coding agents?

Ascenda measures the supervision, interruptions, switching and after-hours work that AI agents create for engineers, reported as cohort aggregates of ten or more.

What should engineering managers measure when rolling out AI coding agents?

Measure adoption, the human work agents create, friction and rework, how engineers say the work feels, and delivery outcomes, against a baseline set before the rollout.

How can we measure AI engineering without monitoring individual employees?

Ascenda reports only aggregates of ten or more consenting engineers, with no individual view for managers. Prompts, code and model outputs are never sent.