AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities
arXiv:2508.11126
The paper surveys the emerging field of AI agentic programming, where large language model‑based coding agents autonomously plan, execute, and interact with development tools, and discusses techniques, challenges, and future opportunities.
Abstract
AI agentic programming is an emerging paradigm where large language model (LLM)-based coding agents autonomously plan, execute, and interact with tools such as compilers, debuggers, and version control systems. Unlike conventional code generation, these agents decompose goals, coordinate multi-step processes, and adapt based on feedback, reshaping software development practices. This survey provides a timely review of the field, introducing a taxonomy of agent behaviors and system architectures and examining relevant techniques for planning, context management, tool integration, execution monitoring, and benchmarking datasets. We highlight challenges of this fast-moving field and discuss opportunities for building reliable, transparent, and collaborative coding agents.