Eno Reyes, co-founder and CTO of Factory, emphasizes the need for high-quality code in software development, especially as AI agents gain popularity. Factory aims to create a platform that enables large engineering teams to develop software autonomously while maintaining code quality. Reyes points out that many existing coding agents require users to choose between vendor lock-in or switching IDEs, which limits flexibility. Factory's solution is a model-agnostic coding agent that can operate across various environments without forcing such trade-offs. The article highlights the complexities of harness engineering, which involves managing context limits of large language models (LLMs) and ensuring effective communication with different development tools. Reyes notes that achieving a good harness requires meticulous attention to various optimization factors rather than relying on a single secret. Factory's approach includes identifying multiple signals of code quality, such as successful compilation, passing tests, and proper documentation. This focus on signals helps improve the performance of their coding agents, which can also assist organizations in enhancing their existing codebases. Reyes discusses the tooling essential for AI coding agents, which includes linters, static type checkers, and automated testing frameworks. These tools provide critical feedback on code quality, enabling agents to function autonomously. He explains that human developers often rely on peer reviews for guidance, but scaling with AI agents necessitates a different strategy. The system can autonomously identify and rectify gaps in code quality signals, allowing developers to focus on higher-level concerns like choosing the right tools. Reyes also tackles concerns about low-quality code generated by AI by referencing research that examines the impact of AI on codebases. He asserts that careful integration of AI agents can mitigate the risk of producing subpar code.