The concept of "closed-loop" LLM agent code generation, where the generated code runs and observes its own interactions with users, is gaining traction in software development. This approach allows for more efficient bug fixing, testing, and error handling, as the LLM can learn from its mistakes and improve over time. The idea is to use system prompting to enable an LLM to generate Semgrep rules, test them, and fix errors, potentially automating tedious tasks like predicting potential issues and manually testing codebases. This could revolutionize the way software development teams work, making it more efficient and effective.