Agentic Workflows are Serverless Applications, so deploy them that way!

Abstract

Accelerating generative AI adoption has driven the expansion of data centers, which amass GPUs, DRAM, and SSDs to feed emerging, resource-hungry AI workloads. The serverless cloud model offers a path to improve application resource efficiency by loading instances on demand. However, the suitability of emerging AI workloads for serverless remains insufficiently explored. We survey the state-of-the-art in serverless hosting for LLM applications and find that: (1) Despite advances in serverless LLM hosting, model loading and initialization processes still dominate startup latency. (2) Agentic AI workloads have not yet been characterized under the serverless context. We propose a deployment scheme for agentic workloads tailored for serverless, accompanied by pre-warming policies that minimize the idle resource footprint and startup latencies. This paper outlines promising research directions for serverless agents.