We added a feedback store to support recorded outcomes. This gives the system a place to persist feedback signals about retrieval and answer behavior.
Feedback is not the same as proof. It becomes useful when tied to reproducible examples, ground truth, and failure categories.
We shaped Agentic RAG evaluation cases around follow-ups, weak evidence, source targeting, and complex multi-part questions.
Ingestion guardrails were added to make document handling and chunk quality more explicit before retrieval uses the content.
QA audit and benchmark tooling was added to separate measurable behavior from impression-based assessment.