Part

Factory metrics

You might say: delivery measures, factory dashboard

The measures that show whether automated work flows, waits, fails, and recovers in a useful way. They connect agent output to delivery and review capacity.

What it does

Turns run and pull request history into evidence for throttle, staffing, gate, and task changes.

Ask

What moved faster, what failed, and where did a person have to wait?

What to look for

1. A dashboard of merge, lead-time, failure, and queue readings
2. A time series split by task type or trigger
3. A review queue with age and volume

Parts

• Merge rate: The share of agent-assisted pull requests that merge during a stated period.
• Lead time: The elapsed time from a code commit to a successful production deployment.
• Change failure rate: The percentage of production deployments that cause a failure.
• Review queue: The count and age of pull requests waiting for an automated or human review.

Sources

1. DORA, A history of DORA’s software delivery metrics, From four keys to five (2024)measuring these factors over time gives teams a high-level understanding of their performance, regardless of their technology stack.
2. Addy Osmani, Agentic Code Review, What the 2026 data actually showsReviewers simply could not keep pace with the volume, so code began merging unread, and that became normal.

Use it on your project

teardown-apply: hold "Factory metrics" (factory-metrics) from the teardown at https://teardownatlas.com/software-factory/ against my project

Paste it into a coding agent that has the teardown skills.

An AI agent wrote this map with the teardown skill. It can contain errors: check the sources before you rely on a part. About and legal