This topic introduces the role of AgentControl configs in LaunchDarkly Experimentation. Experimentation lets you measure how config variations affect end-user behavior using the metrics you define. By connecting metrics to your configs, you can compare variations and decide which variation to serve.
Each config includes a Monitoring tab in the LaunchDarkly user interface (UI). This tab displays performance data if you track AI metrics in your SDK, such as input and output tokens or total call duration to your LLM provider. To learn more, read Monitor config performance.
In contrast, Experimentation lets you measure how your application changes affect end-user behavior, based on signals like page views and clicks. For example, you might use the Monitoring tab of a config to identify which variation consumes the fewest output tokens. But to determine which variation results in the most clicks in your chatbot, you need to run an experiment.
To get started with Experimentation in the context of AgentControl, explore the following resources:
You cannot run a guarded rollout and an experiment on the same flag at the same time. To monitor a variation rollout for regressions, use a guarded rollout. To measure how a variation affects a metric, use an experiment.