DESIGN AND IMPLEMENTATION OF PLUG-AND-PLAY ARTIFICIAL INTELLIGENCE AGENTS USING SERVERLESS ARCHITECTURES

Abstract

The rapid adoption of artificial intelligence (AI) in corporate environments has exposed a recurring gap between experimental prototypes and production-ready systems. While large language models, computer vision, and machine learning pipelines are increasingly accessible, organizations continue to face challenges related to scalability, cost control, operational complexity, and integration when attempting to deploy AI solutions as consumable services. This paper presents a serverless architectural approach for building modular, plug-and-play AI agents deployed as APIs, emphasizing real-world constraints encountered in production environments. The proposed architecture leverages containerized functions, stateless execution, and consumption-based billing to enable rapid deployment, scalability, and economic efficiency. Additionally, the paper discusses architectural trade-offs observed between REST and HTTP API integration models and presents empirical lessons learned from deploying multiple AI agents across natural language processing, computer vision, and data automation domains.