Evaluate a Claude Managed Agent against a written rubric, a set of checkable criteria, and guide improvements within a fixed attempt limit. After a version passes, prepare separate evaluation cases to check performance beyond the examples used for revision.
Phase 3 of building a Claude Managed Agent — the bounded grade→iterate loop. Define a CMA outcome (a required markdown rubric graded by an isolated grader), read each verdict, decide the next move (sharpen / re-run / promote to schedule), and once a version passes, run held-back eval cases in parallel. Use when the user says "grade my agent", "make it pass the rubric", "iterate until it's good", "is it good enough", or when the orchestrator routes phase=grade-iterate. outcome_builder.py builds the user.define_outcome payload (rubric required, max_iterations clamped 1..20 — never unbounded); verdict_reader.py reads the grader result and recommends the next move; eval_scaffold.py generates held-back cases + a parallel run plan (capped at the 25-thread CMA ceiling). Distinct from stage-launch (first launch) and run-without-you (scheduling).
Review the instructions the agent will receive before you install.
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.
python3 scripts/outcome_builder.py \
--sheet ./my-agent/build-sheet.json --max-iterations 5 \
--out ./my-agent/payloads/outcome.json
max_iterations is clamped to 1..20. Send the
payload as a user.define_outcome event (append to the running session).python3 scripts/verdict_reader.py --result ./my-agent/last-verdict.json
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".python3 scripts/eval_scaffold.py \
--sheet ./my-agent/build-sheet.json --out ./my-agent/eval.json --concurrency 5
goal_state.py set --phase run-without-you.max_iterations cap.scripts/outcome_builder.py — user.define_outcome payload (rubric required, cap 1..20).scripts/verdict_reader.py — grader result → next move.scripts/eval_scaffold.py — held-back cases + parallel run plan (≤25 threads).No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Audit web applications for accessibility issues against WCAG 2.2 Level A and AA, the web accessibility standards. Get framework-specific code fixes, contrast checks, verification steps, and reports, with options for automated checks in development pipelines.
Plan A/B tests that compare page variations, from headlines to pricing layouts. Define a hypothesis, choose success metrics, calculate sample size and duration, and prepare launch checks and results reports to support decisions.
Create paid ad headlines and body copy tailored to each platform's character limits. Use existing performance data to develop variations, explore new messaging angles, and check copy for length, quality, and potential rejection risks.
Review code changes from three critical perspectives: production failures, maintainability, and security. Get a consolidated report that ranks findings by severity and provides merge guidance, helping examine assumptions that an ordinary self-review may overlook.
Audit and improve content intended for citation in AI answers. Review evidence, author information, and structure, generate revised content and structured metadata, and keep a local record of observed citations across AI services.
Create signed records for consequential agent actions such as deployments, deletions, and payments. Decide which actions need evidence, record the agent, action, target, and policy, then verify the resulting certificate offline to detect later changes.