An LLM though doesn’t truly understand the goal AND it frequently gets into circular loops it can’t get out of when the solution escapes its capability rather than asking for help. Hopefully it’ll get fixed but some of this stuff is an architectural problem rather than just iterating on the transformer idea.
That's totally true, but it's also a small amount of Python code in the agent scaffolding to ensure that it bails on those kinds of loops. Meanwhile, for something like Semgrep, the status quo ante was essentially no Semgrep rules getting written at all (I believe the modal Semgrep user just subscribes to existing rule repositories). If a closed-loop LLM setup can successfully generate Semgrep rules for bug patterns even 5% of the time, that is a material win, and a win that comes at very little cost.
