Agentic Autoscaling through Worker-Pool Orchestration for LLM-driven Text Classification in Cloud Computing Environments

The growing adoption of large language model (LLM)-based systems for large-scale text processing has created a critical need for dynamic autoscaling to manage high-latency, bursty, and computationally intensive workloads. This paper proposes an agentic autoscaling framework through worker-pool orche