Meta’s new ‘Muse Wonder’ model: good for AI agents, bad for design

Founder takeaway

Evaluate AI models against the distinct jobs in your workflow rather than treating overall capability as interchangeable. A model that performs well on agentic tasks may still be a poor choice for design work.

Why it matters

Adopting a model based on broad performance claims can lower output quality in specialized tasks. A small, task-specific evaluation helps you choose the right model—or combination of models—before embedding it in your product or operations.

Relevant guides

Put it to work

List 10–20 representative tasks for each critical workflow, define what a good result looks like, and run candidate models under the same conditions. Which model should own each task based on observed quality rather than headline claims?