SUPR
Automating Pipeline and Cloud Deployment using Large Language and Large Action Models
Dnr:

NAISS 2025/22-370

Type:

NAISS Small Compute

Principal Investigator:

Amirhossein Layegh Kheirabadi

Affiliation:

Kungliga Tekniska högskolan

Start Date:

2025-03-07

End Date:

2026-04-01

Primary Classification:

10208: Natural Language Processing

Allocation

Abstract

This project explores the automation of Continuous Integration/Continuous Deployment (CI/CD) pipeline creation and cloud deployment through the integration of Large Language Models (LLMs) and Large Action Models (LAMs). Current DevOps workflows rely heavily on manual configuration and scripting, which can introduce inefficiencies and inconsistencies in software deployment. By leveraging the reasoning and generative capabilities of LLMs and the structured action execution of LAMs, this research aims to develop an intelligent system capable of automating key DevOps tasks, including CI/CD pipeline generation, application containerization, and cloud deployment. The proposed system will enable developers to specify deployment requirements using natural language, which will then be interpreted, synthesized, and executed autonomously. This work contributes to the advancement of AI-assisted DevOps by investigating the feasibility, accuracy, and efficiency of LLM-LAM-driven automation in real-world software engineering practices.