LaunchDarkly, a pioneer in feature management, has unveiled AgentControl—a groundbreaking solution designed to handle the unique challenges of AI agents in production. As organizations rapidly deploy generative AI, they face a critical problem: AI behaves probabilistically, not deterministically. Traditional monitoring and rollback strategies fall short. AgentControl offers a runtime layer to manage, observe, and safeguard AI agents in real time. Here are ten essential insights into this new offering.
1. What Is AgentControl?
AgentControl is LaunchDarkly’s newest product, tailored specifically for the agentic AI era. It provides a real-time control layer that sits between your AI models and the end-user experience. Unlike static deployment pipelines, AgentControl enables dynamic adjustments—modifying agent behavior, prompts, or parameters without redeploying code. This fills a critical gap for engineering teams moving AI from testing into live production environments.
2. Why Generative AI Needs a Different Approach
Traditional software is deterministic, meaning the same input always yields the same output. Generative AI, however, is probabilistic—small changes in context can produce wildly different results. This unpredictability makes standard QA and rollback procedures inadequate. AgentControl directly addresses this by offering a runtime environment where you can steer, pause, or alter agent outputs on the fly.
3. The Testing-to-Production Gap
AI models that perform flawlessly in controlled test environments often fail in production. The probabilistic nature of LLMs means that real-world user inputs can trigger unexpected behaviors. AgentControl bridges this gap by enabling real-time monitoring and intervention. Teams can instantly apply guardrails or revert to a safe state without a full redeployment, ensuring that agents behave reliably as usage patterns evolve.
4. Real-Time Runtime Control
At its core, AgentControl is a runtime control layer. It allows developers to change how an agent responds to specific inputs, adjust its tone, enforce content policies, or even limit its capabilities—all while the agent is live. This is a major shift from the traditional deploy-and-hope model. With AgentControl, you can experiment with different configurations and roll back problematic changes in seconds.
5. Extending Feature Flag Concepts to AI
LaunchDarkly built its reputation on feature flags—toggles that let teams gradually release new features. AgentControl extends that concept to AI agents. Instead of toggling a UI element, you can toggle an agent’s persona, knowledge cutoff, or safety filters. This allows for progressive delivery of AI behavior, reducing risk while accelerating innovation.
6. Safety and Governance Built In
One of the biggest concerns with AI agents is safety—they can generate harmful, biased, or incorrect outputs. AgentControl provides centralized governance policies that apply across all agents. You can set rules for what the agent is allowed to say, which data sources it accesses, and how it handles sensitive information. These policies are enforced at runtime, giving compliance teams confidence even as agents adapt.
7. Observability for Probabilistic Systems
Understanding why an agent responded in a certain way is notoriously difficult. AgentControl brings observability tools designed for probabilistic systems. It logs every decision path, prompt variation, model response, and override. This deep visibility helps developers troubleshoot issues, spot drift in behavior, and audit interactions for quality assurance.
8. Progressive Delivery for AI Agents
Rolling out a new AI agent to all users at once is risky. AgentControl supports canary releases and gradual rollouts. You can expose a new agent version to a small percentage of users, monitor its performance, and gradually increase traffic if it behaves well—or instantly roll it back if issues arise. This mirrors best practices from feature management but is adapted for the non-deterministic nature of AI.
9. Seamless Integration with Existing Workflows
AgentControl does not require a complete overhaul of your current stack. It integrates with LaunchDarkly’s existing platform and APIs, so teams already using feature flags can quickly adopt AI agent management. The familiar dashboard and SDKs lower the learning curve, enabling rapid deployment without sacrificing control.
10. Paving the Way for the Agentic Era
As AI agents become more autonomous and widespread, the need for runtime management will only grow. LaunchDarkly’s AgentControl sets a new standard for how enterprises can safely and confidently deploy generative AI at scale. It moves beyond static model hosting to offer an interactive, supervisory layer that aligns agent behavior with business goals and user trust.
In summary, AgentControl from LaunchDarkly tackles the core challenges of deploying probabilistic AI in production. By providing real-time runtime control, safety governance, and progressive delivery, it equips teams to manage the unpredictable nature of generative agents. For any organization building with AI, this runtime layer is quickly becoming essential infrastructure.