AI agents are powerful, but they can introduce real risk in production.
When you ship an agent, you aren’t just deploying code. You’re deploying prompts, models, tools, and decision logic that can change behavior in real time. The challenge is staying in control after release.
In this video, we walk through how AI Configs helps teams orchestrate and safeguard AI agents at runtime.
You’ll see how to create multiple agent variations by swapping models, prompts, and tools without redeploying. We also show how to connect evaluation metrics (like accuracy, relevance, and toxicity) to guarded rollouts. If performance drops below a defined threshold, the system can automatically revert to a trusted version. If toxicity spikes, a guardrail can trigger before a problematic response reaches customers.
Finally, the video explores how telemetry, evaluation metrics, and business KPIs come together inside experimentation dashboards. This allows teams to choose the best agent configuration based on performance, cost, and real user impact.
Watch the video to see what runtime control for agents actually looks like.
Visit this page for an informative walkthrough of the full LaunchDarkly platform, including straightforward examples of the features that help teams gain runtime control.
