AI coding tools are helping teams generate and ship more code than ever, but that speed often creates a new problem: AI code sprawl. As feature flags, targeting rules, segments, and change requests multiply, teams can end up with inconsistent naming, missing ownership, risky production rollouts, policy drift, and manual review bottlenecks that slow everyone down.

In this webinar, we’ll show how the new Harness Feature Management and Experimentation (FME) support for Harness Policies helps teams bring governance to feature delivery without sacrificing velocity. You’ll see how policy-as-code guardrails can automatically validate flag metadata, enforce safer rollout rules, reduce segment-related data risks, and streamline approvals across teams with account, organization, and project-level inheritance. We’ll also demo how developers get real-time feedback when a policy is violated, so they can fix issues before risky changes reach production. The result is a more scalable way to control AI-driven release sprawl, reduce blast radius, and keep delivery fast, measurable, and safe.

Key Takeaways:

• How to control AI-driven code and flag sprawl with automated policy guardrails instead of manual review bottlenecks

• How Harness FME policies can enforce safer rollouts, stronger ownership, cleaner flag hygiene, and reduced segment/PII risk

• How real-time policy feedback and progressive delivery practices help teams reduce release risk without slowing developer velocity