Developers are increasingly turning to AI coding agents like Claude Code to accelerate development, but initial outputs often require refinement before production deployment. While tools like Cursor and Claude Code can rapidly generate powerful applications, the code they produce frequently contains quirks and imperfections that could cause serious issues in production environments.

To address this, developers need specific techniques to ensure AI-generated code meets production standards. The process involves two critical phases: establishing robust initial code generation and implementing thorough review mechanisms. Claude Opus 4.6 and similar models can produce code matching human quality, but only when developers provide proper context and follow systematic approaches.

The article outlines practical strategies for achieving production-ready code, including maintaining Claude.md and Agents.md files to guide AI behavior, using plan mode for thorough requirements gathering, and implementing comprehensive testing protocols. A dedicated PR review skill helps catch repository-specific issues and past mistakes. By combining these approaches, developers can leverage AI coding agents' speed while maintaining the quality standards necessary for production deployments.