More AI code.Less release risk.

Pilot an AI-built reporting workspace with one customer's admins. Keep members and other accounts on the existing path.

Try a search rewrite with an opt-in beta or a regional pilot. Use the same limited audience across related feature flags.

Start an AI-assisted checkout with a small canary. Review errors and completion before more users receive the candidate.

When a new checkout or exporter breaks, restore the working path through a flag change while your team prepares the fix.

Gate an AI-written calculator or parser behind a flag. Validate with a small audience and keep the existing implementation ready.

Try a search limit or integration timeout with a pilot audience. Restore a known-good value when the new setting causes failures.

Compare two experiences with a stable feature flag split. Follow Exposure and Measuring through the primary metric, posterior distribution, and guardrails.

Explore the Release Health design: connect metric sources, define guard conditions, send webhook alerts on threshold breaches, and investigate with release context.

Connect health alerts to an OFF trigger or restore a previous rollout through the REST API, FeatBit CLI, or MCP, such as returning from 20% to 10%.

Deploy AI-assisted changes early, then schedule a reviewed pilot for a time when monitoring and recovery owners are ready.

Manage FeatBit resource definitions as Terraform IaC. Use Git to review the configuration, apply a saved plan, and validate runtime behavior across environments.