1.10 Real-world feedback loop
1.10 Real-world feedback loop
structured signal from user reviews, support tickets, bug reports, meeting notes, and app store ratings enters the agent's context pool. The focus is *signal quality*: bug reports enriched with environment / version / reproduction; meeting notes processed with participant / topic metadata; reviews classified by feature area. A raw "it's broken" note is noise; a structured repro is focus. Ingestion is the last boundary before external content becomes persistent agent context; both instruction-shaped content (`PL4-prompt-injection-defence`) and personal information (`PL4-pii-masking`) are sanitised here, not downstream. (Automation of the ingestion loop itself is scored in `PL5-signal-driven-tasks`.)
Levels
Level 0
No feedback capture; raw noise in inboxes and Slack
Level 1
Some signal captured but unstructured; raw tickets, unparsed reviews
Level 2
Signal is enriched / structured before entering context: bug reports carry env / version / repro; reviews classified; meeting notes tagged with participants and topics. Ingestion sanitisation is two-layered — instruction-shaped content handled per `PL4-prompt-injection-defence`, and PII stripped or pseudonymised at the ingestion boundary per `PL4-pii-masking` (raw personal data from feedback sources does not enter persistent agent context)
Level 3
Structure quality improves over time; enrichment rules evolve from retrieval patterns; signal-noise ratio measured and trends down