Authors

Ian Dougherty, Natalie Lambert, Joshua Wang, Ethan Xu, Reto Achermann and Alexandra Fedorova

Venue

SIGOPS Op. Syst. Rev.

Links

[ .doi ] [ .pdf ] [ .bib ]

Abstract

Bibtex

@article{Dougherty:2026:AgenticWorkflows,
 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.},
 address = {New York, NY, USA},
 author = {Dougherty, Ian and Lambert, Natalie and Wang, Joshua and Xu, Ethan and Achermann, Reto and Fedorova, Alexandra},
 doi = {10.1145/3830422.3830426},
 id = {Dougherty:2026:AgenticWorkflows},
 issn = {0163-5980},
 issue_date = {July 2026},
 journal = {SIGOPS Operating Systems Review},
 month = {7},
 number = {1},
 numpages = {11},
 pages = {30–40},
 publisher = {Association for Computing Machinery},
 series = {SIGOPS Op. Syst. Rev.},
 title = {Agentic Workflows are Serverless Applications, so deploy them that way!},
 url = {https://doi.org/10.1145/3830422.3830426},
 volume = {60},
 year = {2026}
}