The thesis addresses the limitations of Large Language Models (LLMs) in handling long-term, complex tasks such as maintaining contextual continuity and interacting with external systems and APIs. While LLMs have shown remarkable capabilities in natural language understanding and generation, they often struggle with multi-step reasoning, dependency management, and robust integration with cloud platforms. To overcome these challenges, the project proposes the design and implementation of a custom Multi-Agent System (MAS), where LLM-based agents work collaboratively to autonomously orchestrate AWS cloud services in response to natural language instructions. The platform, developed in collaboration with Data Reply, aims to support software developers throughout the Software Development Life Cycle (SDLC) by automating tasks such as cloud infrastructure setup, service configuration, deployment, and monitoring.