Operations | Monitoring | ITSM | DevOps | Cloud

DORA metrics in the AI era reveal a paradox: PR volume is climbing, but deployment frequency is staying flat. In this talk, GitKraken's Director of Product Jeff Schinella breaks down why AI-accelerated code generation is creating a review bottleneck that your DORA metrics can't fully explain on their own. Jeff walks through how PR metrics (cycle time, first response time, code churn, and PR size) serve as the leading indicators behind your DORA data. If your deployment frequency is flat while PR counts go up, the bottleneck isn't your devs. It's your review capacity.

Investigations are time-sensitive, and analysts shouldn’t waste time recreating the same workflows or rewriting familiar queries. Whether troubleshooting infrastructure, investigating suspicious IPs, or analyzing host activity, teams often rely on duplicating old processes and copying query snippets — a slow, inconsistent approach that’s hard to scale.

This example hows how to use Harness CI/CD to build a Cobol application and deploy it to a mainframe running z/OS. We leverage IBM DBB and Wazi Deploy in this example.