Three Generations of Autoscaling — And Why Agentic Traffic Breaks All of Them

Established autoscaling strategies, built for predictable human-driven traffic, are proving ineffective against the rise of AI agents. This new "agentic traffic" is fundamentally different, arriving in instantaneous, unpredictable bursts that overwhelm both anticipatory and reactive scaling models. A modern solution requires shifting from scaling on lagging indicators like CPU to monitoring request velocity and patterns to detect and quarantine runaway agent loops. This approach is complemented by an intelligent AI gateway that acts as a shock absorber, using semantic caching and cost-based throttling to manage demand before it ever impacts expensive core systems.