Knowledge Retrieval Architectures for AI-Assisted Software Development: From Static Context to Autonomous Development Agents

Abstract

The integration of Large Language Models (LLMs) into software development workflows has fundamentally transformed how developers interact with knowledge repositories, codebases, and documentation. However, traditional knowledge retrieval mechanisms face significant challenges when applied to the dynamic, multi-modal, and contextually-rich environment of software engineering. This survey provides a comprehensive analysis of knowledge retrieval architectures specifically designed for AI-assisted software development, examining the evolution from static context provision to autonomous development agents. Building upon this systematic examination, the paper delineates critical research directions that advance the theoretical and practical foundations of AI-assisted software development.