If you ever work with LLMs you know that they quite frequently give up.

Sometimes it's a

    // TODO: implement logic

"this feature would require extensive logic and changes to the existing codebase".

Sometimes they just declare their work done. Ignoring failing tests and builds.

You can nudge them to keep going but I often feel like, when they behave like this, they are at their limit of what they can achieve.

If I tell it to implement something it will sometimes declare their work done before it's done. But if I give Claude Code a verifiable goal like making the unit tests pass it will work tirelessly until that goal is achieved. I don't always like the solution, but the tenacity everyone is talking about is there

> If you ever work with LLMs you know that they quite frequently give up.

If you try to single shot something perhaps. But with multiple shots, or an agent swarm where one agent tells another to try again, it'll keep going until it has a working solution.

Using LLMs to clean those up is part of the workflow that you're responsible for (... for now). If you're hoping to get ideal results in a single inference, forget it.