Phase 3, Grade → Iterate (the bounded loop)

This is the plugin's loop: CMA's outcome primitive self-grades the agent's work in an isolated context and feeds failing verdicts back for the next attempt. It is always bounded by max_iterations (1..20), never "improve forever".

See ../../references/loops-and-workflows.md and the outcome section of ../../references/cma-primitives.md.

Workflow

1. Define the outcome.

   python3 scripts/outcome_builder.py \
     --sheet ./my-agent/build-sheet.json --max-iterations 5 \
     --out ./my-agent/payloads/outcome.json
   

   The rubric is required; max_iterations is clamped to 1..20. Send the payload as a user.define_outcome event (append to the running session).
2. Read every verdict first.

   python3 scripts/verdict_reader.py --result ./my-agent/last-verdict.json
   

   Tables the rubric outcome and recommends: SHIP (satisfied), SHARPEN then re-run (needs_revision), ESCALATE (max_iterations_reached / failed), RESUME (interrupted). With ≤1 iteration left it flips to "make the single highest-value fix or escalate now".
3. Loop invariant. Each iteration must move ≥1 rubric line fail→pass, or the run halts at the cap and escalates. Don't burn the budget on cosmetic edits.
4. Once a version passes, run held-back eval.

   python3 scripts/eval_scaffold.py \
     --sheet ./my-agent/build-sheet.json --out ./my-agent/eval.json --concurrency 5
   

   Held-back cases (never seen during iteration) run in parallel, capped at the 25-thread CMA ceiling, each graded against the same rubric.
5. Decide. SHIP as v0, or promote to a scheduled deployment (Phase 4). Record the verdict on the goal: goal_state.py set --phase run-without-you.

Hard rules