FEEDBACK

Persists operator feedback linked to interactions / workflows per policy.

LiveCatalogue status

Execution activeRuntime posture

Structured human correction capture on meaningful outputs.

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Expanded capabilities

Capture structured human corrections on meaningful outputs
Persist FeedbackRecord rows linked to interactions/workflows
Feed learning governance without auto-applying model changes
Stay tenant-scoped with policy-aware write paths
Support SENTINEL and console correction loops

How they learn & improve

FEEDBACK is the human teaching channel: every correction becomes a candidate for LEARNING — never a silent overnight model swap.

Record operator corrections with interaction provenance.
Route candidates into learning governance for review.
Improve subsequent assists only after explicit human apply.

How they work together

FEEDBACK closes the loop between SENTINEL/RESPONSE outcomes and LEARNING/MNEMOSYNE apply targets.

Learning governance (global promotion)Turns corrections into reviewable promotion packets.
SENTINELCaptures wrong-assist corrections at the operator edge.
MNEMOSYNEMay receive memory-targeted applies after approval.
AUDITKeeps correction capture attributable.

Practical benefits

Wrong assist → better next timeOperators mark a bad suggestion; after review/apply, similar cases get sharper guidance.
No silent retrainingCorrections sit as candidates until someone with authority applies them.
Audit-friendly teachingYou can show who taught the system what — essential for regulated teams.

Learning and improvement stay human-visible: candidates are RECORD_ONLY until an operator reviews and applies them. Sensitive sends, publishes, and approvals never run silently.

FEEDBACK

Expanded capabilities

How they learn & improve

How they work together

Practical benefits

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