Orchestrate and safeguard AI agents with AI Configs

AI agents, while powerful, pose significant risks when deployed in production, as they involve dynamic elements like prompts, models, and decision logic that can alter behavior in real time. This video explains how AI Configs can help teams manage and safeguard AI agents at runtime by allowing them to create multiple agent variations through model, prompt, and tool swaps without the need for redeployment. It demonstrates the integration of evaluation metrics like accuracy, relevance, and toxicity into guarded rollouts, enabling automatic reversion to trusted versions if performance declines or toxicity increases. The video also highlights the role of telemetry, evaluation metrics, and business KPIs in experimentation dashboards, which aid teams in selecting the most effective agent configuration based on performance, cost, and user impact. For a comprehensive understanding of runtime control for agents, viewers are encouraged to explore the full capabilities of the LaunchDarkly platform.