How do AI coding agents work and what are their capabilities?

AI coding agents combine LLMs with development tools to autonomously write, test, debug, and modify code. The agent receives a task description, plans the implementation approach, reads relevant source files, writes or modifies code, runs tests, and iterates until the task is complete. Tool capabilities include file system operations for reading and writing code, terminal execution for running builds and tests, search tools for finding relevant code across the repository, and web search for documentation lookup. Advanced coding agents understand project structure and conventions, generate appropriate test cases, handle multi-file changes with consistent refactoring, resolve build and test failures through iterative debugging, and create pull requests with clear descriptions. Leading coding agents include Claude Code, GitHub Copilot Workspace, Cursor Composer, and Devin. The key challenge is ensuring code quality and safety since the agent may introduce bugs or security vulnerabilities. Human review of agent-generated code remains important.