When DevOps first started to manifest over a decade ago, a lot of the focus was on streamlining engineering, much in the same way as assembly lines. Starting with the most repetitive and automatable processes, we created pipelines for continuous operations––testing, integration and deployment (AKA CI/CD). Concepts were borrowed from Kaizen (Japanese assembly lines) to ensure these processes flowed and worked, and it’s hard to imagine ever going back on the process and quality improvements brought on by CI/CD. But it only got us so far.

After solving for automation of programs - tests, integrations, builds and deploys are all basically scripts - our attention can turn to much heavier sources of friction, which are also harder to automate. These are the touch points between humans: pickup time for code reviews, who do I even assign this review to?!, what do I need to line up to get this PR merged? And between humans and programs: flaky builds and tests, builds + tests that take too long, post-deployment ownership during outages, synchronizing access to dev environments, and much more.

With GenAI getting embedded in the SDLC, the human / machine interface points are even more important. We will soon have 10x the amount of [AI generated] code, with agents writing code, tests & reviews. How will our processes and pipelines deal with this new scale? Who will review and approve all this code? How do we deliver it in a safe manner?

Enter the DevOps for the GenAI age. We must rethink our processes and eliminate the amounting friction. In this talk we’ll bring data and research from millions of PRs and developers, and share how simple tweaks and automations have unlocked velocity by orders of magnitude.

Methods such as automating trivial code fixes, better understanding of ownership and stakeholders in the process, improved communication (the backbone of DevOps!) all map to greater velocity and developer happiness, and are crucial to reap the benefits of the GenAI revolution in the SDLC.

Join us to learn how.