Feedback Loop Fixture v0
Deterministic next actions from replay state and evaluator evidence.
The Single Idea
Deterministic local feedback recommendations were derived from learner event replay state, evaluator evidence, and available eval-plan decisions. This emits a re-ranked recommendation contract without mutating the stored planner output.
01
Snapshot
4records
1advance
1next modules
1re-ranked paths
02
Recommendations
| State | Action | Current | Recommended | Blockers |
|---|---|---|---|---|
| decision_generated | await_practice_submission | - | - | practice_submission_missing |
| decision_generated | await_practice_submission | - | - | practice_submission_missing |
| evaluated_pass | advance_after_model_review | Notice recurring sales work and name the judgment needed. | Use AI for first-pass drafts, summaries, and structured sales prep. | none |
| decision_generated | await_practice_submission | - | - | practice_submission_missing |
03
Boundary
This is deterministic local feedback routing. It does not mutate the stored planner output, claim adaptive learning, or use real beta telemetry; it emits a re-ranked recommendation contract from replay and evaluator evidence.
Where to start
Use this artifact to decide whether the next runtime move is advance, revise, evaluate, or wait.
Use this artifact to decide whether the next runtime move is advance, revise, evaluate, or wait.