DORA Primer for AI Delivery Teams
DORA gives teams a shared language for delivery performance. In AI-assisted development, that matters even more: faster coding only helps when the platform keeps quality and flow stable.
The four DORA metrics
- Deployment frequency — how often you ship to production.
- Lead time for changes — commit to production elapsed time.
- Change failure rate — percent of deployments causing incidents, rollbacks, or hotfixes.
- Mean time to restore (MTTR) — how quickly service recovers after failure.
Together these show both speed and stability. Optimize all four; over-optimizing one metric usually creates hidden drag elsewhere.
What good looks like
- Frequent, small deployments instead of risky batch releases.
- Predictable lead time with fewer queue bottlenecks.
- Low failure rate through tests, policy checks, and safe rollout patterns.
- Fast recovery through clear alerts, runbooks, and ownership.
AI-specific anti-patterns
- More code, same platform: AI output increases PR volume without improving CI/CD and observability.
- Local speed, global slowdown: developers move faster but release approvals and incident handling become chokepoints.
- Metric theater: tracking output (lines, prompts, PR count) instead of outcome (DORA).
30-day improvement loop
- Baseline the four metrics weekly.
- Pick one bottleneck (for example, review wait time).
- Ship one platform change (automation, guardrail, or dashboard).
- Compare DORA movement after two release cycles.
For capability planning, continue with the AI capabilities guide. For signal quality and instrumentation, read the observability primer.
Run this yourself: GitHub repo link