What should an AI Agent do when encountering sudden traffic spikes?

When encountering sudden traffic spikes, an AI Agent should dynamically scale resources and optimize request handling to maintain performance and service availability. This ensures reliable responses without significant disruptions.

Key principles include autoscaling computational capacity based on real-time demand, implementing queueing mechanisms to manage inflow, and prioritizing critical tasks to prevent bottlenecks. Necessary conditions are a cloud-based or scalable infrastructure, continuous monitoring for early detection, and predefined thresholds for auto-adjustments. Precautions involve avoiding overprovisioning to control costs, ensuring fault tolerance to handle partial failures, and testing resilience under simulated loads to minimize downtime risks. The scope applies to any AI-driven service, such as chatbots or recommendation engines, facing unexpected user surges.

Actual steps involve detecting spikes through monitoring tools, scaling resources like compute instances via APIs, directing excess traffic to queues, and deprioritizing non-essential requests. In scenarios like e-commerce sales events, this maintains user experience, reduces abandonment rates, and supports business continuity by preventing revenue loss through efficient resource use.