Product Q&A assistant
Size, compatibility and spec questions answered from catalogue and return data; stock and price verified.
Product Q&A assistant is an agent scenario in the Retail & E-commerce sector running on the web, app, whatsapp channels. Trigger: Question typed into the chat widget on a product page. The Shopping Supervisor coordinates 3 specialist agents (Intent Agent, Catalogue Agent, Reply Agent) across 4 systems (Vector Store, PIM / Catalogue, Shopify / OMS, Web Chat Widget); of 9 steps, 0 write to systems and 1 are checks or approvals. Outcome: Sourced answer and the right size/product card in chat, one tap to add to basket.
Product Q&A assistant
- Web chat: "Would size 42 of this coat fit me? 1.78 m, 80 kg"
- Intent: size fit · product SKU 88-4410 · height 178 · weight 80
- Plan: size chart → return data → stock → grounding check → answer
- Size chart and fit note retrieved ("slim fit")
- PIM: 80% wool, lined; return data: size 42 "too small" 31%
- Recommendation: size 44 (slim fit + return data), confidence 86%
- Stock: size 44 available (7), Istanbul warehouse, next-day delivery
- Answer grounded in catalogue sources only; price and delivery claims verified
- Answer and size-44 product card sent to chat with an "add to basket" button
- ■completed
Simulation · derived from real agent definitions · every agent can be built by dialogue with the Autonomous Agent and validated with a test corpus
Question typed into the chat widget on a product page
Sourced answer and the right size/product card in chat, one tap to add to basket
Agents
Shopping Supervisor
Decomposes the objective, delegates to agents, manages approval points, merges the result.
Intent Agent
Extracts the question and constraints (height, weight, device model)
detect_intentextract_product_refextract_constraintsCatalogue Agent
Builds the recommendation from catalogue, size chart and return data
search_catalogueget_product_specsget_size_chartcheck_compatibilityget_stockReply Agent
Grounds the answer in sources and sends it with a product card
check_groundingcompose_answeradd_product_cardssend_replySteps
| # | Kind | Agent | What happens | System | ms | tok |
|---|---|---|---|---|---|---|
| 01 | ingest | Shopping Supervisor | Web chat: "Would size 42 of this coat fit me? 1.78 m, 80 kg" | — | 160 | — |
| 02 | reason | Intent Agent | Intent: size fit · product SKU 88-4410 · height 178 · weight 80 | — | 380 | 240 |
| 03 | plan | Shopping Supervisor | Plan: size chart → return data → stock → grounding check → answer | — | 220 | 160 |
| 04 | retrieve | Catalogue Agent | Size chart and fit note retrieved ("slim fit") | Vector Store | 340 | — |
| 05 | tool | Catalogue Agent | PIM: 80% wool, lined; return data: size 42 "too small" 31% | PIM / Catalogue | 420 | — |
| 06 | reason | Catalogue Agent | Recommendation: size 44 (slim fit + return data), confidence 86% | — | 720 | 810 |
| 07 | tool | Catalogue Agent | Stock: size 44 available (7), Istanbul warehouse, next-day delivery | Shopify / OMS | 300 | — |
| 08 | verify | Reply Agent | Answer grounded in catalogue sources only; price and delivery claims verified | — | 360 | 280 |
| 09 | notify | Reply Agent | Answer and size-44 product card sent to chat with an "add to basket" button | Web Chat Widget | 240 | 210 |
Outfit stylist: a complete look by size, stock, budget and style
The customer sends a photo and a sentence; the agent builds three looks that are in stock, fit and within budget, and adds the chosen one to the cart.
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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- Your 2–3 priority problems
- Live demo of a similar scenario
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