Enforcing rules and managing expectations for AI agents with CI and code review (rubyonai.com)

A recent blog post highlights the importance of enforcing rules and managing expectations for AI agents in coding, particularly focusing on the integration of continuous integration (CI) and code review processes. The author shares a past experience with software failures due to incomplete checks, which underscores a common challenge in working with large language models (LLMs) that often produce inconsistent outputs. To address this, the post advocates for implementing deterministic checks in local CI workflows, utilizing tools like Rubocop, Prettier, Brakeman, and RSpec to ensure code quality and security. The significance of these measures lies in their potential to greatly enhance the reliability of AI-generated code. By implementing a structured three-stage code review process that verifies compliance with specifications, checks for adherence to project conventions, and assesses overall code quality, the post emphasizes that AI agents can improve their output and contribute to more robust software development practices. The author encourages a management approach that fosters trust in AI agents while maintaining oversight through established rules, ultimately aiming for seamless integration of AI in the coding process.