Kavli Affiliate: Huawei Zhang
| First 5 Authors: Huanting Wang, Huanting Wang, , ,
| Summary:
AI agentic programming is an emerging paradigm in which large language models
(LLMs) autonomously plan, execute, and interact with external tools like
compilers, debuggers, and version control systems to iteratively perform
complex software development tasks. Unlike conventional code generation tools,
agentic systems are capable of decomposing high-level goals, coordinating
multi-step processes, and adapting their behavior based on intermediate
feedback. These capabilities are transforming the software development
practice. As this emerging field evolves rapidly, there is a need to define its
scope, consolidate its technical foundations, and identify open research
challenges. This survey provides a comprehensive and timely review of AI
agentic programming. We introduce a taxonomy of agent behaviors and system
architectures, and examine core techniques including planning, memory and
context management, tool integration, and execution monitoring. We also analyze
existing benchmarks and evaluation methodologies used to assess coding agent
performance. Our study identifies several key challenges, including limitations
in handling long context, a lack of persistent memory across tasks, and
concerns around safety, alignment with user intent, and collaboration with
human developers. We discuss emerging opportunities to improve the reliability,
adaptability, and transparency of agentic systems. By synthesizing recent
advances and outlining future directions, this survey aims to provide a
foundation for research and development in building the next generation of
intelligent and trustworthy AI coding agents.
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