AI coding tools are helping teams produce more features faster, but they are also multiplying what happens after the code is written: more changes to test, more rollout decisions to make and more release risk to manage. Harness’s core thesis is that change is the atomic unit of risk, and as AI increases code volume, practitioners need a better way to make every change measurable, reversible and observable before it reaches everyone.
In this webinar, we’ll show how teams can use experimentation to validate AI-generated code in the real world instead of relying on guesswork, delayed feedback or broad production rollouts. By combining experimentation with delivery pipelines and policy guardrails, practitioners can deploy safely, release progressively to the right users, measure impact on technical and business metrics, and stop or roll back when results are off track. The session will include a live demo showing how Harness helps teams move from AI-generated output to controlled, validated release decisions.
Key Takeaways:
- How experimentation helps validate whether AI-generated features actually improve outcomes before broad release
- How pipelines and progressive delivery reduce blast radius with canary rollouts, targeted exposure and rollback controls
- How policies enforce safer defaults, governance and approval workflows so teams can scale feature delivery without losing control
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