AI AgentE-commerce Agent
E-commerce Agent that automates sales workflows using your data and tools.
The challenge
Shoppers abandon baskets over questions the product page does not answer, and the same fit, compatibility and order-status queries fill the service inbox afterwards. Answers depend on the agent, delivery promises are made from stale data, and return reasons never reach the merchandising team.
The outcome
Azure AI Search retrieves specifications and reviews semantically, Azure OpenAI answers with the source cited, and a Microsoft Foundry agent completes the order against live catalogue and stock APIs. Disputes and goodwill decisions reach a human with the full order history already on screen.
At a glance
- Type
- ai agents
- Category
- industry
01 — Architecture
End-to-end architecture
A shopper arrives on the storefront, app or a social channel. The Foundry agent searches the catalogue semantically, answers fit and compatibility questions from real product data and reviews, and completes the order. Disputes and goodwill decisions go to a human agent with the order history already on screen.
- Shopper: Shopper / Merchant
- Storefront: 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 Blob Storage
- Outcome & insight: Dynamics 365 / CRM, Agent escalation, Power BI / Fabric
02 — Workflow
Process & decision workflow
How a shopper is guided from intent to a fulfilled order and beyond — discover, advise, convert, fulfil and serve, then branch. Standard requests resolve automatically, while a dispute or a goodwill decision goes to a human agent with the policy and the history attached.
- Discover: Intent understood and the catalogue searched
- Advise: Size, fit, compatibility and stock explained
- Convert: Basket assembled with eligible promotions applied
- Fulfil: Order placed, tracked and proactively updated
- Serve: Returns, exchanges and order queries handled
- Learn: Conversion, drop-off and return reasons tracked
- Path 1 · standard request — Resolve automatically: Order, return or answer completed
- Path 2 · dispute or goodwill — Human agent decides: Order history and policy shown for the call
03 — Components
Key Microsoft components
Commerce answers have to match live price, stock and policy — search, conversation and fulfilment all stay on the Microsoft stack.
Azure Static Web AppsStorefront, chat surface and self-service order pages.
Azure Communication ServicesOrder, delivery and returns notifications by email or SMS.
Azure API ManagementSecure gateway for catalogue, pricing, order and payment APIs.
Azure Container AppsAgent runtime that scales through peak trading.
Microsoft Foundry Agent ServiceGuidance, resolution and tool-calling orchestration.
Azure OpenAI modelsProduct answers, comparisons and conversational copy.
Azure AI SearchSemantic search over catalogue, specifications and reviews.
Azure AI Content SafetyGuardrails on product claims, tone and regulated categories.
Azure Logic AppsOrder, return, refund and fulfilment workflows.
Azure SQLOrders, returns, refunds and customer service records.
Azure Cosmos DBLive session, basket and conversation state.
Azure Blob StorageProduct media, specification sheets and assets.
Azure Machine LearningPropensity, recommendation and return-risk models.
Power BI / Microsoft FabricConversion, deflection and return-reason 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
- Semantic catalogue search
- Attribute and fit matching
- Personalised recommendation
- Retrieval-augmented generation
- Promotion eligibility
- Order status resolution
- Return-reason classification
- Multilingual conversation
- Sentiment-aware escalation
- Conversion analytics
- Workflow orchestration
AI responsibility boundaries
The agent sells and serves inside published policy, quoting only live price, stock and delivery. It does not hold card data, invent product claims, or approve goodwill and dispute outcomes. Anything outside policy or emotionally charged goes to a human agent with the full order history attached.
05 — Personalization
Personalization & evolving process
The same methodology applies to every agent in the catalog. Tune the catalogue, the journey template, the policy rules and the value model — the page structure stays identical.
Catalogue & market profile
Define the categories, markets, languages, currencies and delivery options in scope. The personas are the shopper, the customer service agent, the merchandiser and the returns team.
Journey template
One consistent flow for every commerce agent: discover, advise, convert, fulfil, serve after the sale, escalate when needed and feed the signal back to merchandising.
Policy & claim rules
Declare the returns and refund policy, promotion rules, delivery promises, approved product claims and the goodwill authority. A promise the systems cannot support is never made.
Value model
Capture baseline metrics first, then map the expected benefits: conversion, basket size, contact volume, time to resolve, return rate and customer satisfaction.
06 — Impact
Key outcomes & business impact
Starting targets for the value case — validate each one against the customer baseline during discovery.
- Conversion+12%Fit and compatibility questions answered before checkout.
- Contact deflection40%+Order and returns queries resolved without an agent.
- Return handlingMinutesReturns authorised and labelled inside the conversation.
- Claim traceability100%Every product answer cites the specification behind it.
Illustrative improvement index
Manual baseline = 100. Illustrative targets, not a commitment — confirm against the customer baseline.
- Contact volume: manual baseline 100, AI-assisted target 60
- Time to resolve: manual baseline 100, AI-assisted target 20
- Return processing: manual baseline 100, AI-assisted target 45
Related & recommended
Derived automatically from our solution knowledge graph.
Related AI agents
Other agents that pair well with this one.
AI AgentOrder Returns Resolution
Agent that verifies who owns an order before disclosing anything about it, separates a statutory remedy from a discretionary one, and authorises only a reversible return that a named agent settles
AI AgentCustomer Service Copilot
Grounded assistant that resolves and deflects support tickets
AI AgentPatient Appointment Scheduling
Agent that verifies identity and eligibility, proposes clinically appropriate slots, holds one for a human scheduler to confirm and drafts consented reminders
AI AgentProperty Viewing Coordination
Agent that qualifies a viewing enquiry, checks access constraints and occupier notice, proposes slots on objective availability alone and holds one for a human agent to confirm
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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.
Related managed services
Keep it running and optimised.
ManagedAI Managed Services
AI Managed Services from our UAE-based 24/7 Cloud Operations Center.
ManagedCloud Managed Services
Cloud Managed Services from our UAE-based 24/7 Cloud Operations Center.
ManagedDevOps Managed Services
DevOps Managed Services from our UAE-based 24/7 Cloud Operations Center.
ManagedFinOps / Cloud Cost Management (OpsNow)
FinOps / Cloud Cost Management (OpsNow) from our UAE-based 24/7 Cloud Operations Center.
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