auto-iteralisted

# Auto Itera ## Overview **Autonomous experimentation engine for AI engineering decisions.** You provide three things — a goal, the candidate approaches, and a success threshold. auto-itera does the rest: sources real production data, scores baseline + arms in parallel, diagnoses per-row, iterates in disciplined sprints, runs the held-out test pass, and writes a one-page conclusion doc with publication-quality charts. The pivot: **AI engineering decisions move from handcrafted trial-and-error to autonomous scientific search.** Instead of opening a notebook and eyeballing diffs, you delegate the whole experimental loop and get back a verdict you can defend in a code review. Internally the loop is a six-phase scientific method — **Frame → Source → Metric → Run → Diagnose → Conclude** — with sprint-and-generalize refinement (not unbounded iteration, not arbitrary hard caps). The autonomy lives in phases 1, 3, 4, and 5; phases 0 (Frame) and 2 (Metric pre-registration) are the user-provided inputs that anchor the search. **Honest expectation boundary**: auto-itera autonomously *runs* the experiment given a goal and candidate arms. It does **not** autonomously brainstorm what to test — you (or the human asking Claude) still write the candidate prompts / pick the candidate models / define the metric. The autonomy is "from candidates to verdict", not "from blank slate to verdict". ```mermaid flowchart LR F["Phase 0<br/>Frame"] --> S["Phase 1<br/>Source + Split"] S --> M