LaunchDarkly is the leading runtime control platform designed for the AI era, providing engineering teams with the ability to manage code and AI agents in production safely. Created to de-risk releases and enable systems to self-heal and optimize, the platform moves control from deployment time to runtime. It provides developers and AI agents with the necessary visibility and authority to manage features, experiment continuously, and govern AI agent behavior without needing to redeploy code.

The platform operates as a centralized control layer that integrates seamlessly with existing development stacks. By utilizing feature flags, users can control which features or agent behaviors are exposed to specific segments of users, enabling progressive rollouts and instant rollbacks. LaunchDarkly extends this capability to the AI lifecycle through AgentControl, which allows for the real-time management of prompts, models, and tools used by AI systems. It is engineered for global scale, serving over 50 trillion flag evaluations daily with sub-200ms configuration propagation.

Some of the key features are:

• Feature Management: Robust feature flagging system that allows for granular targeting, segmentation, and progressive release of code.
• AgentControl: Specialized management for AI agents, allowing users to update prompts, models, and tools at runtime without redeployment.
• Automated Rollbacks: Real-time monitoring of performance thresholds that triggers immediate reverts to previous known-good states if an issue is detected.
• Observability: Deep integration of telemetry, session replays, and logs with feature flags and release versions to diagnose root causes instantly.
• Experimentation: Native A/B/n testing and multi-armed bandit support that runs directly on live traffic to optimize performance and outcomes.
• Self-Healing Systems: Intelligent guardrails that allow systems to automatically remediate failing behavior or drift in AI agents.
• Agent Native Integration: Support for Model Context Protocol (MCP), CLI tools, and IDE extensions to manage control workflows directly from within developer environments.

LaunchDarkly is used by deploying its native SDKs across your architecture. Once integrated, teams use the centralized dashboard or CLI/MCP tools to define, update, and monitor behaviors in real-time. Changes are propagated to the application immediately, providing a seamless feedback loop between production monitoring and configuration management.

Some common use cases include:

• Releasing AI-generated code: Safely shipping and verifying code produced by AI tools using gradual rollouts and automated health checks.
• Governing AI Agents: Keeping agent behavior on track by monitoring for drift and adjusting responses in real-time to mitigate bad behavior.
• Optimizing Performance and Cost: Dynamically routing traffic across different AI models and prompt variants based on real-time performance and budget metrics.
• Continuous Experimentation: Running experiments on live traffic to optimize UI components, AI prompts, and business logic simultaneously.
• Building Self-Healing Systems: Automatically detecting failures in production and triggering remediation workflows without requiring human intervention.