Review response and quality escalation
Reviews classified and answered in brand tone; recurring defects joined with return data and escalated to QA.
Review response and quality escalation is an agent scenario in the Retail & E-commerce sector running on the web, api channels. Trigger: Hourly review pull (site, marketplace, app store). The Voice-of-Customer Supervisor coordinates 3 specialist agents (Classification Agent, Response Agent, Quality Agent) across 3 systems (Review Platform API, Shopify / OMS, Jira (QA)); of 10 steps, 1 write to systems and 2 are checks or approvals. Outcome: Reply published, exchange link with the customer, defect cluster open with QA and evidence. Approval rule: 1-star review replies and defect escalations → community manager approval.
Review response and quality escalation
- Hourly pull: 184 new reviews, 23 rated ≤ 2 stars
- Review #R-77120: 1 star, "zip broke in 2 weeks" → defect signal, SKU 55-2210
- Plan: order context → response draft → defect cluster → approval → QA escalation
- Order #99841 delivered 18 Aug, under warranty, exchange available
- Response drafted: apology + free exchange offer, brand tone, 68 words
- SKU 55-2210: 17 "zip" reviews in 30 days, return rate 9.4% (average 3.1%)
- Rule: ≥ 10 similar defects or return rate > 2× average → QA escalation
- 1-star reply → community manager approved from the queue
- Jira QA-1184 opened with supplier batch, 17 reviews and return data attached
- Reply published on the platform with the exchange link included
- ■completed
Simulation · derived from real agent definitions · every agent can be built by dialogue with the Autonomous Agent and validated with a test corpus
Hourly review pull (site, marketplace, app store)
Reply published, exchange link with the customer, defect cluster open with QA and evidence
Agents
Voice-of-Customer Supervisor
Decomposes the objective, delegates to agents, manages approval points, merges the result.
Classification Agent
Detects sentiment, topic and defect signal
classify_sentimentdetect_topicsdetect_defect_signalResponse Agent
Drafts an on-brand reply using order context
get_order_contextdraft_responsecheck_tonepublish_replyQuality Agent
Clusters defects, compares with return rate, opens the QA ticket
cluster_defectsget_sku_return_ratecreate_qa_ticketSteps
| # | Kind | Agent | What happens | System | ms | tok |
|---|---|---|---|---|---|---|
| 01 | ingest | Voice-of-Customer Supervisor | Hourly pull: 184 new reviews, 23 rated ≤ 2 stars | — | 260 | — |
| 02 | reason | Classification Agent | Review #R-77120: 1 star, "zip broke in 2 weeks" → defect signal, SKU 55-2210 | — | 520 | 380 |
| 03 | plan | Voice-of-Customer Supervisor | Plan: order context → response draft → defect cluster → approval → QA escalation | — | 240 | 170 |
| 04 | tool | Response Agent | Order #99841 delivered 18 Aug, under warranty, exchange available | Shopify / OMS | 340 | — |
| 05 | reason | Response Agent | Response drafted: apology + free exchange offer, brand tone, 68 words | — | 680 | 620 |
| 06 | tool | Quality Agent | SKU 55-2210: 17 "zip" reviews in 30 days, return rate 9.4% (average 3.1%) | Shopify / OMS | 380 | — |
| 07 | verify | Quality Agent | Rule: ≥ 10 similar defects or return rate > 2× average → QA escalation | — | 420 | 310 |
| 08 | approval | Voice-of-Customer Supervisor | 1-star reply → community manager approved from the queue | — | 2,200 | — |
| 09 | write | Quality Agent | Jira QA-1184 opened with supplier batch, 17 reviews and return data attached | Jira (QA) | 460 | — |
| 10 | notify | Response Agent | Reply published on the platform with the exchange link included | Review Platform API | 320 | — |
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Where is my order? (WISMO)
Customer asks, order and carrier events join instantly; on delay a new ETA is given and a trace opened.
Returns and refunds
Policy and history checked in seconds, label issued; the refund executes when the carrier scans the parcel.
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