Coding Agent is a class of AI-based automated software development systems, typically centered on Large Language Models (LLMs), which assist or autonomously complete engineering tasks such as code generation, program debugging, defect fixing, and software maintenance by understanding natural language requirements, analyzing codebases, invoking development tools, and executing testing tasks. Unlike traditional code completion tools, coding agents can perform multi-step planning around target tasks and continuously adjust their execution process based on feedback from the external environment.

The development of coding agents is built upon code large models, agent frameworks, and software engineering automation technologies. In 2024, researchers from Princeton University and other institutions published the paper SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering, proposing the agent framework SWE-agent designed for software engineering tasks. This study introduced the "Agent-Computer Interface" (ACI), enabling large language models to interact with code repositories, terminal environments, and development tools through specially designed operation interfaces, performing code modifications, testing, and issue fixing in real GitHub software engineering tasks, providing an important reference for subsequent coding agent research.

Coding agents are primarily used to address issues that arise when traditional large language models directly generate code, such as insufficient context understanding, lack of environmental feedback, and difficulty handling complex engineering tasks. Their core workflow typically includes task understanding, code retrieval, modification planning, code editing, test execution, and iterative optimization based on feedback. By connecting compilers, terminals, version control systems, and testing frameworks, coding agents can form a closed-loop development process of "generate—execute—evaluate—correct."

In recent years, with the improvement of code capabilities in large language models, coding agents have been gradually applied in software development assistance scenarios. Representative tools include LLM-based code editing and development assistants such as Claude Code launched by Anthropic, as well as Cursor Agent developed by Anysphere. These systems can help developers complete code writing, code understanding, debugging analysis, and automation of engineering tasks, but they still require human supervision to ensure the correctness, security, and engineering reliability of the generated code.

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