Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
Data Science
Towards Data Science·Aug 17·5 min read
Three Generations of Autoscaling — And Why Agentic Traffic Breaks All of Them
TL;DR
Autonomous agents have disrupted traditional autoscaling methods, which relied on capacity planning for over two decades. New strategies are needed to effectively manage this shift in traffic patterns.
✦ Why It Matters
Evaluate your current autoscaling strategy and consider integrating machine learning for better adaptability.
Key Takeaways
- Shift from CPU-based scaling to request velocity monitoring.
- Implement semantic caching to reduce model call costs.
- Use async queuing to manage agent traffic without immediate response expectations.
- Adopt token-based admission control to prioritize resource usage.
How It Works
The proposed four-layer response includes behavior-based scaling that detects request patterns indicative of agent loops, allowing for early intervention. An AI gateway manages costs by caching similar requests and throttling excessive usage.
Async queuing decouples request handling from immediate responses, accommodating the different expectations of human and agent interactions. Finally, token-based admission control limits resource consumption based on the compute cost of requests rather than their count.