• Sandeep Keshetti
• A Renuka

Integrating legacy systems with cloud environments has posed a significant challenge, especially for businesses that wish to modernize their IT infrastructure without disrupting ongoing operations. Legacy solutions have turned to middleware and API gateways to enable communication between legacy applications and cloud services. Such solutions, however, do not support requirements for scaling, flexibility, and automation. With swift developments in cloud-native architectures like microservices and serverless computing, as well as growing capabilities of Large Language Models (LLMs), a new solution for integrating legacy systems is on the horizon. The main research gap is using contemporary LLMs to create smart API proxy layers that integrate legacy and cloud systems, improve data flow, improve performance, and automate complicated integration tasks. While some studies have studied API proxies and microservices for legacy system integration, few have addressed using LLMs in these proxy layers to dynamically manage requests, react to changes, and simplify error handling and documentation. Additionally, issues of security, privacy, and compliance for LLM-driven proxies have not been extensively researched. This research aims to fill these gaps by introducing a new framework for LLM-driven API proxy layers. The framework will use advanced natural language processing and machine learning technology to improve how legacy system data is converted into cloud-native formats, enable intelligent routing and caching, and automate documentation and error handling. This approach aims to make it much simpler to integrate legacy systems with contemporary cloud environments, allowing organizations to adopt and scale cloud solutions more effectively.

LLM-powered API proxy, legacy system integration, cloud environments, microservices, serverless architecture, data transformation, intelligent routing, automation, error handling, cloud-native APIs, legacy modernization, API gateways, security and compliance, natural language processing, machine learning.

"Designing an LLM-Powered API Proxy Layer for Integrating External Legacy Systems in Cloud Environments", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.h673-h696, March-2025, Available :http://www.jetir.org/papers/JETIR2502781.pdf

"Designing an LLM-Powered API Proxy Layer for Integrating External Legacy Systems in Cloud Environments", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. pph673-h696, March-2025, Available at : http://www.jetir.org/papers/JETIR2502781.pdf