The runtime control layer for the AI era.

FAQ

• What is runtime control?

  AI is generating and changing software faster than teams can validate it before release. Traditional workflows only allow control before deployment, and release practices remain largely manual: Engineers still configure controls, manage rollouts, and respond when things go wrong for each change. That creates more release toil and makes it harder to apply consistent safeguards. Runtime control lets teams manage behavior after deployment so they can help safely adapt to real-world conditions in real time.

• Why do teams need runtime control?

  AI is generating and changing software faster than teams can validate it before release. Traditional workflows only allow control before deployment. Runtime control lets teams manage behavior after deployment so they can help safely adapt to real-world conditions in real time.

• How is LaunchDarkly more than feature flags?

  Feature flags are the foundation—they let you control how code is released.

  LaunchDarkly extends this into a full runtime control platform. In addition to release control, it provides real-time visibility into how changes behave and the ability to take action instantly in production.

  This means you’re not just controlling releases; you’re continuously controlling, monitoring, and optimizing both code and AI systems after they’re deployed.

  For code, Release Autopilot automates the work of releasing. It classifies each change by risk, flags the ones that warrant control, rolls them out, rolls them back if something goes wrong, and cleans up afterward—so engineers don’t have to manually set up flags, coordinate rollouts, or clean up.

• What does “control at runtime” mean?

  Control at runtime means making changes to how systems behave while they are running in production—without redeploying code. This includes controlling exposure, adjusting behavior, and responding to issues instantly based on real-time data.

• How does LaunchDarkly support AI use cases?

  LaunchDarkly controls both the AI-generated code you ship and the AI agents you run in production.

  For code, Release Autopilot classifies each change by risk, flags the ones that warrant control, releases them progressively, and rolls back when configured performance thresholds are breached—or notifies your team instead.

  For agents, LaunchDarkly lets teams control how AI agents behave in production. You can manage prompts and models, monitor behavior in real time, and adjust outputs instantly—without redeploying—so you can help safely iterate on AI systems using real user data.

• How is LaunchDarkly different from AI evaluation or observability tools?

  AI evaluation and observability tools help you understand what’s happening.

  LaunchDarkly lets you take action on it.

  Instead of just monitoring or analyzing behavior, you can instantly adjust prompts, routing, and system behavior in production—without redeploying—so you can respond to real-world conditions as they happen.

  For code changes, Release Autopilot connects measurement directly to action: When observability signals a degradation, it can pause or roll back the change automatically, limiting its impact in production without a redeploy.

• Can I use LaunchDarkly without AI?

  Yes. LaunchDarkly is widely used for feature management, experimentation, and release safety across traditional applications. AI support extends these capabilities to modern AI-driven systems.

• What can I control at runtime with LaunchDarkly?

  You can control how code and AI agents behave in production, including feature releases, user targeting, prompts, model behavior, and system responses, so you can adapt instantly based on real-time conditions.