RAG-Based AI Agents for Enterprise Software Development: Implementation Patterns and Production Deployment

Abstract

The integration of artificial intelligence (AI) agents into enterprise software development has emerged as a transformative approach to enhance productivity and code quality. Retrieval-Augmented Generation (RAG) represents a paradigm shift in how AI systems access and utilize knowledge, combining the generative capabilities of large language models (LLMs) with dynamic information retrieval mechanisms. This review paper examines the current state of RAG-based AI agents in enterprise software development contexts, focusing on implementation patterns and production deployment strategies. We analyze the architectural foundations of RAG systems, including vector databases, embedding models, and retrieval mechanisms that enable AI agents to access up-to-date code repositories, documentation, and organizational knowledge bases. The paper explores various implementation patterns such as code completion agents, automated testing assistants, documentation generators, and intelligent code review systems. We investigate production deployment challenges including scalability, latency optimization, security considerations, and integration with existing development workflows. Through systematic analysis of recent research and industrial applications, we identify key success factors for deploying RAG-based AI agents in enterprise environments, including context window management, retrieval accuracy, and hallucination mitigation strategies. The review also addresses emerging trends such as multi-agent collaboration, fine-tuning strategies for domain-specific tasks, and hybrid retrieval approaches. Our findings suggest that while RAG-based AI agents demonstrate significant potential for transforming enterprise software development, successful deployment requires careful consideration of organizational context, data privacy requirements, and continuous model evaluation frameworks.