6 Feedback Management AI Agents, Live in Production.
6 live agents automate feedback management within customer service. 1 runs on a schedule and 5 run on demand. Each one publishes the inputs it needs, the steps it works through and what it hands back.
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Customer Feedback Analysis
Read a batch of survey responses and surface the themes, the drivers of low scores, and what is worth acting on.
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Customer Testimonial Request
Find the customers whose own words are worth asking to quote publicly, and draft each ask so it names what you want to quote and how consent works. Every draft is approved individually — nothing is sent.
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Feedback Intake Routing
Take feedback arriving from every channel and route each item to the team that can actually act on it, separating a product request from a support failure from something that needs answering today.
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NPS Detractor Follow-Up
Work out which detractors are worth a personal reply and which are better left alone, then draft each one from what they actually wrote. Every draft is approved individually — nothing is sent.
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Product Review Request Check
Check a planned review-request campaign before it goes out: whether the list was filtered by expected sentiment, whether anything is being offered in exchange, and whether the wording steers the score.
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Service Survey Designer
Build a survey that will actually tell you something — questions derived from what you need to decide, with the leading ones, the double-barrelled ones and the unanswerable ones stripped out.
Asking only the customers worth asking, and only what you need to decide
Feedback programmes generate a great deal of data and very few decisions. The survey is usually the culprit: questions accumulate over years, several are leading, some are double-barrelled so the answer is uninterpretable, and a few ask about things the respondent has no way of knowing. The output is a score that moves without anyone being able to say why. Routing is the second failure — feedback arrives from several channels into one queue, so a product request, a support failure and something that needs answering today all wait together, and the ones needing action get the same attention as the ones needing only counting. Then there is the follow-up problem: contacting an unhappy customer is often right and sometimes makes things distinctly worse, and the difference is not obvious from the score alone.
These agents fix the instrument and the routing before the analysis. Service Survey Designer builds a survey from what you actually need to decide and strips the leading, double-barrelled and unanswerable questions. Feedback Intake Routing separates a product request from a support failure from something urgent and sends each to the team that can act. Customer Feedback Analysis then surfaces themes, the drivers of low scores, and what is worth acting on. For outreach, NPS Detractor Follow-Up decides which detractors merit a personal reply and which are better left alone, drafting each from what they actually wrote. Customer Testimonial Request finds the customers whose own words are worth quoting and drafts an ask that names what you want to quote and how consent works. Product Review Request Check audits a planned campaign for the things that make review solicitation improper: a list filtered by expected sentiment, something offered in exchange, or wording that steers the score.
What this moves
- Feedback reaching a team that can act on it
- Items are routed by what they actually are — a product request, a support failure, or something needing an answer today — instead of arriving in one undifferentiated inbox.
- Survey responses that inform a decision
- Questions are derived from what you need to decide, with the leading, double-barrelled and unanswerable ones stripped out before the survey goes out.
- Outreach that makes a detractor angrier
- Which detractors are worth a personal reply and which are better left alone is decided before anything is drafted, and every draft is approved individually.
Customer Service
How AI agents handle feedback management
Drawn from the 6 agents above — what they require, how they run, and what comes back.
What they need
- Survey export
- Analysis settings
- Candidate customers
- Request settings
- Feedback received
- Routing rules
- Follow-up settings
- Planned recipient list
What comes back
- Satisfaction
- What the responses say
- Themes
- Words that recur
- Worth acting on
- This batch
- Batch summary
- Drafted asks — approve each individually
How they run
- Run on demand
- 5
- Runs on a schedule
- 1
- Steps per run
- 2–4
- Credits per run
- 5–9
Where feedback management fits in customer service
Most tickets are about one customer’s specific order or account. An agent that can read that state actually fixes the problem, instead of replying with a help article the customer already found.
Next Step
Deploying feedback management agents
These run as-is against the inputs listed above. Most deployments adapt one — a different source system, a different tolerance, a different approval path. The first call establishes which.