A Multi-Agent Coding Assistant for Cloud-Native Development: From Requirements to Deployable Microservices

The rapid adoption of cloud-native architectures has created an urgent demand for automated development tools that can translate natural language requirements into deployable cloud-native microservices. While recent advances in large language models (LLMs) have enabled AI-assisted code generation, existing approaches predominantly focus on isolated code completion tasks rather than end-to-end software delivery. This paper presents CloudMAS, a multi-agent coding assistant framework that orchestrates specialized agents to transform user requirements into deployable cloud-native applications. Our system comprises six specialized agents: an Architect Agent for service decomposition and API design, three parallel Coder Agents specialized in backend, frontend, and infrastructure-as- code (IaC) generation respectively, a Tester Agent for automated test synthesis and execution, and an Ops Agent for container configuration and Kubernetes manifest generation. These agents are coordinated by a dedicated Orchestrator Agent that manages workflow execution and conflict resolution. We introduce a novel conflict resolution mechanism that enables agents to iteratively refine outputs through structured feedback loops. To address the lack of systematic benchmarks for end-to-end cloud-native development, we construct CloudDevBench, a publicly available evaluation dataset containing 50 real-world development tasks with associated test suites and deployment validation criteria. Experimental results demonstrate that CloudMAS achieves 92% compilation success, 81% test pass rate, and 84% deployment success rate, substantially outperforming single-LLM and single- agent baselines across all metrics.