A Practical Process for Using AI Agents to Review Customer Feedback Without Losing Human Judgment

# Why use an agent but keep a person in the loop

# Start with a bounded, documented scope

Before analysis, remove personal identifiers such as names, emails, phone numbers, account or order numbers, and exact addresses. Replace identifying details with neutral labels like "[customer]" or "[order number]" when relationships matter. Keep the original records protected elsewhere. Write a short scope note listing included sources, date range, filters, redaction rules, and obvious gaps. That note becomes part of the deliverable.

Ask the agent to perform tasks that map to visible patterns: count mentions of a feature, collect exact phrases, group similar topics, and flag frequently used words. Treat those outputs as observations.

Separately, collect interpretations—what customers felt, why a problem happened, and the business impact. Interpretations are useful but not direct measurements. Make both layers visible in results with a simple format:

Observed: 18 comments mention waiting for payment confirmation. Possible meaning: Some customers may not know whether the payment went through. Open question: Are confirmations delayed, hard to find, or missing in a particular situation?

# Keep original evidence beside every theme A label alone ("Checkout is confusing") is too weak. For each important theme, attach several short source excerpts and a safe reference (date or anonymized ID) so someone can find the original without exposing personal data. Excerpts should be long enough to preserve meaning but not so long that private information returns.

# Separate repeated issues, one-offs, and different experiences Have the agent classify entries into at least three kinds:

• Repeated problems that appear across many comments.
• Specific requests or preferences that appear once or a few times.
• Comments that describe a different or positive experience (including "this worked for me").

# Practical checklist for a human-checked review

• Define and document the scope and filters.
• Redact PII and label protected fields.
• Ask the agent for counts, phrase lists, and candidate clusters.
• Attach short excerpts to each cluster and compare labels to evidence.
• Mark observations vs possible meanings and list open questions.

• Use the review to decide next steps, assign owners, and track follow-up.

A human-checked workflow keeps the source evidence close to conclusions and makes the review repeatable and accountable.