Most of what an agent does day to day, extracting fields, scoring against criteria, classifying, ranking, does not need a frontier model. A small model running on your own machine handles it. The expensive intelligence belongs at design time, not run time.
Perian is a local-first framework built on that split. A frontier model plans once: it designs the prompts, builds the evaluation, and judges the output. Small local models then execute, at no marginal cost, privately, on device. When quality needs to lift, a refine-on-demand loop lets a frontier model propose prompt changes offline, and a deterministic gate accepts them only when the measured numbers improve.
The part I care about most is trust. A model never certifies its own work. The local crew builds; a different model family reviews. That is grounded in real findings: same-provider models share most of their errors, so a second opinion from the same family tells you little. Perian pairs a Qwen-family pipeline with a Gemma-family challenger that re-checks each score and cites the specific rule broken.
The evaluation is deliberately self-critical. A six-level capability battery scores each model, and the write-up flags where its own tests are underpowered rather than hiding it. Models audition for roles instead of chasing one leaderboard, which is how a 14B model ends up owning causal reasoning over a larger one.
The job-search reference implementation runs end to end on placeholder data, so the method is inspectable rather than just described. Code: perian and perian-jobsearch.