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Engineering teams consistently say code quality matters: testing, maintainability, and reliability. But when delivery pressure rises, those priorities often slip or are pushed aside in favor of speed.
Now, with AI dramatically increasing code generation, that tension is only intensifying. The result isn’t just technical debt, but hidden defects, fragile systems, and growing production risk at scale.
In this panel, we’ll explore the downsides of traditional approaches to code quality, especially in the continuous integration (CI) stage, where signals exist but are rarely actioned. We’ll discuss how leading organizations are rethinking quality as a continuous, signal-driven practice rather than a static checklist.
Finally, we’ll examine how the rise of AI-generated code is raising the bar for quality systems. As the total number of code changes continues to grow, teams need automated, context-aware approaches that scale with velocity, without adding friction or slowing delivery.
Reinforce quality in the face of AI pressure, by learning:
- How to prioritize quality work using signals from code, CI, and production
- What a modern, CI-centric approach to quality looks like in practice
- How engineering leaders can balance speed and reliability in an AI-driven world
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