A weak model with well-built scaffolding around it can close most of the gap to a frontier model, so fix your code before you upgrade your model.

Scaffolding pays off in inverse proportion to model strength, so the money you save by switching to a cheap model can be recovered by engineering around it.

They built a system called AutoDesign that does this automatically. It runs an agent on real tasks, looks at what went wrong, then rewrites one piece of the surrounding setup: a prompt, a tool, a validation check, a retry rule.

A change survives only if it improves scores on the training tasks and doesn't hurt a held-out set, so the system can't just overfit its way upward.

They tested it on turning papers into conference posters, and released a benchmark, PosterBench, to score them.

The result is that scaffolding is worth more than most people assume, and worth the most to weak models. Seven agents each gained 5 to 19.6 points, with the biggest jumps going to the cheapest models.

So before you upgrade to a pricier model, spend a week improving the checks and retry logic around the one you have.

– arxiv. org/abs/2608.13560

Title: "AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design"