AI AgentRetail Agent
Retail Agent that automates sales workflows using your data and tools.
The challenge
Associates leave the customer to go and check a system for stock, and the answer is often out of date by the time they come back. Store and online inventory disagree, promotions are applied inconsistently, and a sale is lost whenever the size is not on the shelf in front of them.
The outcome
Azure AI Search reads one live stock position across store, warehouse and online, a Microsoft Foundry agent matches products and eligible offers, and Azure Logic Apps fulfils by reservation, collection or ship-from-store. Azure Machine Learning ranks what to suggest next, and exceptions pass to an associate.
At a glance
- Type
- ai agents
- Category
- industry
01 — Architecture
End-to-end architecture
A shopper is met in store, on the app or on the web. The Foundry agent reads the need and the loyalty tier, checks live stock across store, warehouse and online, and matches products and eligible offers — fulfilling across channels and handing exceptions to a store associate with the stock position already on screen.
- Shopper: Shopper / Associate
- Channels: Azure Static Web Apps, Communication Services
- Application: Azure API Management, Azure Container Apps
- AI & agent: Microsoft Foundry Agent, Azure OpenAI models, Azure AI Search, Azure AI Content Safety
- Data & records: Azure SQL, Azure Cosmos DB, Azure Machine Learning
- Outcome & insight: Dynamics 365 / CRM, Store associate, Power BI / Fabric
02 — Workflow
Process & decision workflow
How a shopper is served across channels — understand, check, recommend and fulfil, then branch. Available, in-policy requests complete on the spot, while an exception or a goodwill call goes to a store associate with the stock position and purchase history already visible.
- Greet: Shopper met in store, on the app or on the web
- Understand: Need, occasion, budget and loyalty tier read
- Check: Live stock across store, warehouse and online
- Recommend: Products and eligible offers matched to the shopper
- Fulfil: Reserve, click-and-collect or ship from store
- Learn: Basket, conversion and stock signals captured
- Path 1 · available and in policy — Complete the sale: Reservation or order placed and confirmed
- Path 2 · exception or goodwill — Associate steps in: Stock, history and policy shown for the call
03 — Components
Key Microsoft components
Retail answers are only as good as the stock position behind them — search, recommendation and fulfilment all stay on the Microsoft stack.
Azure Static Web AppsStore, app and web shopping experience.
Azure Communication ServicesOffers, reservation and collection notifications.
Azure API ManagementSecure gateway for point of sale, stock and loyalty APIs.
Azure Container AppsAgent runtime that scales through trading peaks.
Microsoft Foundry Agent ServiceAdvice, availability checking and fulfilment orchestration.
Azure OpenAI modelsProduct answers, comparisons and conversational offers.
Azure AI SearchSemantic search across catalogue, attributes and stock.
Azure AI Content SafetyGuardrails on price, promotion and product claims.
Azure Machine LearningPropensity, recommendation and demand models.
Azure Logic AppsReservation, collection and ship-from-store workflows.
Azure SQLSales, inventory, reservations and loyalty records.
Azure Cosmos DBLive basket, session and conversation state.
Dynamics 365 / CRMCustomer profile, loyalty tier and purchase history.
Power BI / Microsoft FabricBasket, conversion, footfall and stock-gap dashboards.
04 — AI
What the agent consumes
The capabilities the agent applies to every shopper, and the line it does not cross.
AI capabilities embedded in the agent
- Intent detection
- Live stock lookup
- Semantic catalogue search
- Propensity scoring
- Offer eligibility
- Retrieval-augmented generation
- Cross-channel fulfilment
- Loyalty personalisation
- Multilingual conversation
- Price and claim guardrails
- Basket analytics
- Escalation routing
AI responsibility boundaries
The agent advises and sells from live stock and published price, and never promises availability it cannot see. It does not apply discounts outside campaign rules, hold payment data or decide goodwill. Exceptions and disputes go to a store associate with the stock position and purchase history already on screen.
05 — Personalization
Personalization & evolving process
The same methodology applies to every agent in the catalog. Tune the estate, the journey template, the price rules and the value model — the page structure stays identical.
Store & catalogue profile
Define the store estate, catalogue, fulfilment options, loyalty scheme and trading calendar in scope. The personas are the shopper, the store associate, the store manager and the buying team.
Journey template
One consistent flow for every retail agent: greet, understand the need, check live stock, recommend, fulfil across channels, escalate when needed and feed the signal back to buying.
Price & policy rules
Declare the price book, promotion rules, reservation windows, returns policy and the goodwill authority. Availability is quoted from the live position, never from an assumption.
Value model
Capture baseline metrics first, then map the expected benefits: conversion, basket size, lost sales from stockouts, associate selling time and customer satisfaction.
06 — Impact
Key outcomes & business impact
Starting targets for the value case — validate each one against the customer baseline during discovery.
- Basket size+15%Relevant, in-stock additions offered in context.
- Stock visibilityLiveOne position across store, warehouse and online.
- Associate timeSellingLess time spent checking systems for a customer.
- Price traceability100%Every quoted price and offer cites its rule.
Illustrative improvement index
Manual baseline = 100. Illustrative targets, not a commitment — confirm against the customer baseline.
- Time to answer: manual baseline 100, AI-assisted target 20
- Lost sales from stock: manual baseline 100, AI-assisted target 60
- Associate admin: manual baseline 100, AI-assisted target 50
Related & recommended
Derived automatically from our solution knowledge graph.
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Related quick wins
Ready-made Azure AI to start fast.
Technologies
What powers this solution.
TechnologyAzure AI
Managed AI services for vision, speech, language and document.
TechnologyMicrosoft Copilot Studio
Low-code platform to build custom copilots and agents.
TechnologyAzure AI Foundry
Platform to design, evaluate and operate AI apps and agents.
TechnologyAzure OpenAI
Enterprise access to GPT models with governance.
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Keep it running and optimised.
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