Product feedback weekly digest
Group support tickets, issues, and transcripts into recurring product needs and evidence.
What this workflow does
Group support tickets, issues, and transcripts into recurring product needs and evidence. It separates observed facts from inference, surfaces conflicting evidence, and delivers a review-ready finding set instead of an opaque answer.
What you gain
The team sees repeated customer pain without reading every conversation.
What the AI agent changes
How this worked before
Traditional automation could collect records, but a person still had to compare sources, resolve conflicts, and write the conclusion.
What the AI agent changes
The agent can plan a search, inspect multiple sources, distinguish facts from inference, and return a cited report.
Agent trigger
The scheduled weekly product-feedback review starts.
Inputs
- Process context: support tickets, GitHub issues, sales notes, transcripts, and previous themes
- Approved policies, ownership, and exception rules
Agent flow
- 1
Collect source material from the approved systems and record its timestamp.
- 2
Cross-check conflicting signals and separate facts from inference.
- 3
Prepare ranked themes, examples, changed volume, and open questions with citations, unknowns, and confidence.
- 4
Deliver the approved report and preserve its evidence set.
Human decisions
After step 3
The accountable process owner approves ranked themes, examples, changed volume, and open questions.
Outcome
- Cited product feedback digest
- Evidence, exceptions, and audit trail
Guardrails
- Label inference separately from observed facts.
- Do not close the investigation while required sources are unavailable.
Risks and mitigations
Missing or stale evidence can produce a confident but incomplete finding.
Show source coverage, conflicts, timestamps, and unanswered questions.
Sources and evidence
Sources establish feasibility or impact. Not every metric comes from an identical implementation.