The topics in this category explain how to use LaunchDarkly AgentControl to manage your configs. You can use AgentControl to customize, test, and roll out new large language models (LLMs) in your generative AI applications.
An AgentControl config is a single resource that you create in LaunchDarkly to control how your application uses large language models. It lets teams manage prompts, instructions, and model settings outside of application code so they can iterate, experiment, and release changes more safely without redeploying. To learn how to create one, read Create configs.
When you create a config, you select a configuration mode that defines how the model behaves in your application.
AgentControl supports two modes:
Both modes use the same config resource and support variations, targeting rules, monitoring, experimentation, and lifecycle management.
Both completion mode and agent mode can integrate with external tools or APIs. Tool usage depends on how your application and SDK are implemented, not on the selected configuration mode. Agent mode enables structured, multi-step workflows. You can integrate external tools in either mode.
With AgentControl, you can:
AgentControl supports advanced use cases such as retrieval-augmented generation, integration with external tools or APIs, and evaluation in production. You can:
These capabilities let you evaluate model behavior in production, run targeted experiments, and adopt new models safely without being locked into a single provider or manual workflow.
If you use an AI agent to create and manage configs, you can use LaunchDarkly agent skills to help AI coding agents execute common tasks safely and consistently.
AgentControl is an add-on feature. Access depends on your organization’s LaunchDarkly plan. If AgentControl does not appear in your project, your organization may not have access to it.
To enable AgentControl for your organization, contact your LaunchDarkly account team. They can confirm eligibility and assist with activation.
For information about pricing, visit the LaunchDarkly pricing page or contact your LaunchDarkly account team.
Every config contains one or more variations. Each variation defines model settings with messages for completion mode or instructions for agent mode. You define targeting rules to control which variation LaunchDarkly serves to a given context.
In your application, you use one of LaunchDarkly’s AI SDKs to evaluate a config for a given context. The LaunchDarkly SDK evaluates targeting rules and selects a variation. The AI SDK plug-in then uses that variation to return the resolved configuration, including model settings and messages or instructions.
As part of this evaluation, the AI SDK resolves any variables in your prompts using context attributes and additional variables you provide. This enables you to tailor prompts and model settings for each context at runtime. When you update prompts, instructions, or model configuration in LaunchDarkly, those changes take effect immediately without requiring you to redeploy your application.
LaunchDarkly does not invoke model providers on your behalf. Your application is responsible for calling the model provider directly using its own credentials and the configuration returned by the AI SDK. LaunchDarkly does not proxy or independently invoke model providers.
After your application calls the model provider, use the AI SDK to track AI metrics such as generation count, token usage, latency, errors, and evaluation scores. LaunchDarkly aggregates these metrics and displays them on the Monitoring tab.
The topics in this category explain how to create configs and variations, update targeting rules, monitor related metrics, and incorporate AgentControl into your application.
In this section:
In our guides:
In our SDK documentation: