AI Agents for E-Commerce Businesses: Automate Support, Returns, and Admin

E-commerce admin is not one task. It is a pile of small tasks that repeat all day:

• Where is my order? (tracking and delivery estimates)


• How do I return this? (instructions, labels, refund expectations)


• Can you change my address? (pre-fulfilment change windows)


• I received the wrong item (picking errors and reshipments)


• My payment failed (iDEAL/Klarna/credit card edge cases)


• Do you have this in stock? (inventory and backorder messaging)

The operational problem is not that your team cannot answer these. The problem is that answering them steals time from the work that actually grows the business: improving product, improving logistics, negotiating supplier terms, and fixing systemic issues.

Returns create a predictable workflow

In the EU, consumers generally have a 14-day right of withdrawal for distance sales, with specific exceptions. In practice, your return policy translates into a checklist: eligibility window, item condition rules, return label generation, refund method, and communication templates.

For a human agent, a return request is a mini project. For an AI agent, it is a structured flow:

1. Identify the order and items.


2. Check the return window and policy rules.


3. Produce a return label or instructions.


4. Update the order record and notify the customer.


5. Escalate if the case hits an exception (damaged, lost package, high-value item, suspected abuse).

Multi-channel makes everything harder

If you sell via Shopify and also through Bol.com or Amazon, you now have multiple inboxes, multiple payout schemas, and multiple ways customers contact you. The same question arrives in different places, and your team repeats the same answer.

We treat this as a systems problem. The right abstraction is not channel-specific support. It is a single workflow engine that reads the same sources of truth and produces the same actions and messages, regardless of whether the request came from your website chat, an email reply, or a marketplace message.

When we scope an e-commerce agent project, we start by mapping your top 10 ticket types by volume. In most stores, 60–80% of contacts sit in a small set: tracking, returns, and basic product questions. That is the automation starting point.

A note on consumer rules (so messages stay correct)

Many return questions are really questions about rights and timelines. Under EU consumer rules, customers often have a 14-day withdrawal period for distance sales, with specific exceptions (for example: custom-made items or sealed hygiene goods once opened). We do not leave this to improvisation. We turn your policy into explicit rules and approved message templates, so the agent stays consistent and your humans only handle exceptions.

Customer service automation works when the agent can verify facts. We do not want the agent guessing. For e-commerce, verification comes from the order system, carrier status, and policy documents.

Order tracking and delivery questions

A large share of tickets are really one request: reassure me. The agent can do this safely when it can read:

• The fulfilment state (paid, picked, packed, shipped, delivered)


• The tracking number and carrier


• The latest scan events from the carrier


• The expected delivery window you promise on your site

With those inputs, the agent can send a precise reply: the order shipped at 16:02, the latest scan was at the sorting hub at 02:14, delivery is expected tomorrow. If the scan shows “delivery attempted” or “address unknown,” the agent escalates with a short summary and suggested next steps.

Returns and refund flows

Returns are ideal for agentic workflows because they are repetitive and policy-driven. For example, if a customer requests a return within the allowed window, the agent can:

• Confirm eligibility using the order date and policy


• Generate a return label through your shipping tool (for example via Sendcloud)


• Provide packaging instructions and a clear refund timeline


• Update the order record so your warehouse sees it

We add guardrails to avoid expensive mistakes: high-value items may require human approval; partial refunds may require evidence; and the agent should never promise a refund it cannot execute.

Complaint triage and escalation quality

The practical value of an agent is not only the tickets it resolves. It is also the tickets it escalates well.

When a ticket needs a human, the agent should attach:

• The customer’s identity and order ID


• A timeline of events (order placed, shipped, delivered)


• Carrier scan data or payment status


• Policy excerpt that applies


• A proposed resolution option set (refund, replacement, goodwill credit)

This reduces your average handling time even for L2/L3 cases. Humans stop reading long threads and start deciding.

Quality controls we implement

In e-commerce support we typically start in “draft mode” (the agent writes suggested replies). Once accuracy is proven, we allow limited actions (for example: issuing a return label). Fully automated refunds are possible, but only with strict limits and audit logs.

We also run a feedback loop: every correction your team makes becomes training data for your store-specific policy layer. The agent learns your exceptions, not just generic customer service patterns.

Fraud signals and abuse patterns

E-commerce support includes a small but expensive category: abuse. The goal is not to accuse customers; the goal is to spot patterns early and route them to a human. We configure the agent to flag signals such as:

• Repeated “item not received” claims from the same address


• Unusually high return frequency relative to orders


• High-value orders combined with urgent refund pressure


• Mismatches between delivery scans and the customer story

The agent does not decide guilt. It escalates with evidence: order history, carrier scans, and previous tickets.

Chargebacks and payment disputes

Chargebacks are operationally painful because they combine time pressure with incomplete information. A good agent can prepare the dispute pack: order confirmation, proof of delivery, conversation history, and the policy excerpt that applies. Even when a human submits the final response, the preparation work is what consumes time.

Service levels without burnout

If you want to maintain a same-day response promise during peak periods, you either overstaff or you automate. With an agent, we can keep L1 response fast and consistent, then let your human team focus on the cases where empathy and judgement matter.

Support automation is only half the story. E-commerce admin also lives in finance.

A high-volume seller has many transaction types that do not look like traditional bookkeeping:

• Payment provider settlements (Mollie/Adyen) with fees and chargebacks


• Marketplace payouts (Bol.com/Amazon) with commissions, shipping charges, and advertising costs


• Refunds and partial refunds


• Gift cards and store credit


• Multi-currency sales if you sell cross-border

If you do not reconcile these correctly, you end up with messy VAT positions and a year-end cleanup project.

VAT and cross-border basics (so you can plan)

Dutch VAT is simple in the small but gets complex at scale. The core rates you will see most often are 21% standard, 9% reduced, and 0% for certain zero-rated situations. For EU B2C cross-border sales, the EU-wide distance selling threshold of €10,000 is a practical milestone: above it, VAT is generally due in the customer’s country and many sellers use the OSS scheme to report it.

For imports, IOSS is relevant for low-value goods (under €150) to simplify VAT collection. The point is not to memorise every edge case. The point is that your data must be clean enough to support the right treatment.

How a finance agent helps in practice

We usually connect a finance agent to:

• Bank transactions (read-only via PSD2 when possible; consent typically needs renewal periodically)


• Your payment provider exports


• Shopify/WooCommerce order exports


• Marketplace payout statements


• Your accounting system (Exact Online, Moneybird, or a CSV-based process)

The agent then performs a set of repeatable tasks:

• Split payouts into revenue, fees, and VAT components where data allows


• Categorise recurring fees consistently (marketplace commissions, PSP costs, shipping, ads)


• Match refunds to original orders to avoid duplicated revenue


• Flag anomalies (a sudden spike in chargebacks, missing payout files)

This is not a promise that “the AI does your taxes.” You still review and you still submit. But your numbers stay clean during the year instead of becoming a spreadsheet crisis in January.

If your store is doing hundreds or thousands of transactions per day, even small classification errors compound. That is why we treat confidence scoring and correction workflows as first-class features: the agent should show what it is sure about and what it wants you to review.

Records retention (a real operational constraint)

In the Netherlands, businesses generally have an administration retention obligation of 7 years (and in specific cases, such as data related to immovable property, longer). For e-commerce teams this becomes practical: you need to keep invoices, payout statements, and evidence for VAT treatment in a way you can retrieve later.

We design the finance workflow so every classification can link back to a source document (payout statement line, order ID, refund ID). This turns a future audit question into a lookup, not a manual reconstruction project.

Marketplace fees and VAT visibility

Marketplaces and PSPs bundle multiple components into one settlement. If you only book the net payout, you lose fee visibility and you distort your margin. We split net payouts into gross revenue, fees, and refunds where the data allows, and we keep the uncertainty visible so you can review edge cases.

Multi-channel is where most e-commerce teams lose consistency.

A customer asks a question via Instagram DM. Another asks the same thing via email. A third asks via Bol.com messaging. Your team replies three times, with three slightly different answers. That is how policy drift happens.

A well-designed agent sits above the channels. It uses one set of policies and one order data layer, then produces channel-specific outputs.

Channels we commonly support

• Website chat and contact forms


• Email (support@)


• Social inboxes (Instagram, Facebook)


• Marketplace messaging (Bol.com, Amazon)

We do not treat these as four separate projects. We treat them as four connectors.

Why this matters operationally

• A customer should not get a different return window depending on channel.


• Your team should not retype tracking explanations.


• Your warehouse should see return requests in one place.

The agent enforces this by routing every request through the same decision logic. For example: a return request triggers the same eligibility checks regardless of where it came from.

We also add one practical capability that humans rarely do consistently: proactive updates. If a shipment is delayed, the agent can notify affected customers with verified information, reducing inbound tickets before they arrive.

One knowledge base, versioned

To keep answers consistent, we use a single knowledge base for policies, product facts, and shipping commitments. We version it. When your return window changes or a carrier introduces a new delay code, we update the source and the agent follows immediately.

This matters for accountability. If a customer disputes what they were told, you can trace the agent’s message back to the exact policy version used.

Channel constraints are real

Marketplaces often constrain how you communicate (templates, limited links, specific tone expectations). Social DMs are fast but messy, and customers paste screenshots instead of order numbers. The agent helps by extracting the important fields (order ID, email, address) and routing the case through the same workflow, even when the input is unstructured.

E-commerce automation stands or falls on integration quality. If the agent cannot read the order status or write back the return label ID, it will stay in “generic helper” mode and you will not get meaningful savings.

Core systems we integrate with

• Storefront and order system: Shopify or WooCommerce


• Marketplaces: Bol.com and Amazon are common for Dutch sellers


• Payments: Mollie and Adyen are common in the Netherlands


• Shipping and labels: Sendcloud, PostNL, DHL


• Helpdesk: Zendesk, Freshdesk, Gorgias, or a shared inbox


• Accounting: Exact Online, Moneybird, or exports for your accountant

The integration design principle is simple: the agent reads from systems of record and writes only through policy-checked tools.

A practical example: automated return labels

When a customer requests a return, the agent needs four pieces of data:

• The order ID and item(s)


• The return window (policy + order date)


• The customer address and contact details


• The label generation tool credentials

If those are available, the agent can generate the label, attach it to the order, and send the customer instructions.

Security and GDPR

E-commerce data includes personal data (names, addresses, order history). Under GDPR/AVG, you want purpose limitation and access control. We implement least-privilege access tokens per connector, store secrets securely, and log every tool call that touches customer data.

In practical terms: the agent should not have blanket admin access to your Shopify store. It should have scoped permissions to do only what the workflow requires.

This is also why we start with a narrow set of workflows. You earn automation by proving accuracy and control, not by turning everything on at once.

Event-driven automation (webhooks instead of polling)

For stores with volume, we prefer event-driven integrations. Shopify and many tools emit events when orders are created, fulfilled, refunded, or returned. That lets the agent react immediately: send a proactive delay update, open a task for a warehouse exception, or pre-fill a customer reply.

We also build idempotent tool actions. If the same return request arrives twice, the system should create one label, not two.

Separation of concerns

We keep connectors (tokens, API calls, data mapping) separate from the model runtime. That makes audits and access reviews simpler, and it reduces blast radius if anything goes wrong.

Support is one of the easiest places to model unit economics.

A human-handled ticket is expensive. Even if you pay a support agent €3,000–€4,000 per month and they handle 1,000–1,500 tickets, your fully loaded cost per ticket often lands in the €5–€15 range once you include tooling, management overhead, and peak staffing.

With agentic automation, the variable cost per resolved ticket can drop dramatically. We typically model AI-handled tickets in the €0.10–€0.50 range, depending on the model, the workflow, and how much retrieval and tool use happens.

A concrete example

Assume your store receives 200 tickets per day (6,000 per month):

• Human-only at €7 per ticket: 6,000 × €7 = €42,000 per month.


• Agent-assisted with 70% resolution at €0.25 per resolved ticket: 4,200 × €0.25 = €1,050 per month in variable cost.


• The remaining 1,800 tickets go to humans, but with better escalation summaries, so handling time drops.

Even after you add a platform fee and integration work, the economics are obvious. The bigger you are, the faster you feel it.

The key is to treat the automation as a workflow product. If you deploy a generic chatbot that cannot verify orders and cannot execute actions, you will not see these numbers. If you deploy an agent that can read order status, generate labels, and draft accurate replies with citations, you can.

We also track cost per ticket over time. When your policies change or your carrier issues a new status code, the agent must adapt. That is why we keep a review workflow and a measurable accuracy dashboard.

Deflection and handling-time savings both matter

Most teams focus on deflection (tickets fully resolved by the agent). The second lever is just as valuable: faster human handling. If the agent pre-fills the summary and pulls the right order facts, humans spend less time searching and more time deciding.

Using the same 6,000 tickets per month example: if 30% still require a human (1,800 tickets) and the agent saves 4 minutes per ticket by preparing context, that is 7,200 minutes saved, or 120 hours per month. At a modest €30 per hour fully loaded cost, that is €3,600 per month in time saved on top of deflection savings.

We track both levers in the same dashboard: resolution rate, average handling time, and escalation quality.

E-commerce support is not only a cost center. It is part of conversion.

Pre-purchase questions happen outside office hours: sizing, materials, delivery times, warranty, and return rules. If you answer quickly, you often win the sale. If you answer tomorrow, the customer buys elsewhere.

An agent that can answer accurately at 23:30 is a revenue feature, not a nice-to-have.

Where conversion impact shows up

• Product questions that block checkout (compatibility, dimensions, care instructions)


• Delivery time questions (especially near holidays)


• Return policy reassurance (customers buy when they feel safe)

We do not promise a specific conversion lift because every store is different. But we do measure leading indicators:

• First response time for pre-sales questions


• Percentage of pre-sales questions resolved without human involvement


• Abandonment-related contacts resolved within minutes

In Dutch retail, seasonal peaks are predictable: Black Friday, Sinterklaas, and the Christmas period. These peaks bring a wave of “will it arrive on time?” questions. Agents can absorb that wave, keep your human team focused, and maintain consistent messaging.

The main risk with 24/7 automation is tone. That is why we build brand voice constraints and template libraries. The agent should sound like your store, not like a generic support bot.

Support and sales are closer than they look

When a shopper asks “will this fit?” or “can it arrive before Friday?”, that is a sales question disguised as a support ticket. If your agent can answer with verified product specs and delivery commitments, you remove friction from checkout.

We often implement two modes:

• Pre-sales mode: product and delivery questions, with strict grounding in your catalog data and shipping promises.


• Post-sales mode: order-specific questions, grounded in order and carrier data.

This separation prevents overselling. The agent should never invent stock or delivery times.

What we measure

Beyond ticket metrics, we measure pre-sales response time and the percentage of questions resolved in-session. Even if you do not attribute revenue perfectly, you will see the operational change: fewer abandoned chats and fewer “I’ll come back later” emails.

The practical promise of AI agents in e-commerce is simple: you can scale ticket volume without scaling headcount at the same rate.

We have seen stores jump from 50 tickets per day to 500+ tickets per day in peak periods. If you try to cover that with hiring, you either over-hire for the quiet months or you burn out your team during peaks.

Agents are elastic: they do not care if it is Monday morning or Saturday night.

How we scale responsibly

Scaling is not “turn everything on.” Scaling is expanding the allowed workflow set as accuracy proves itself.

A typical rollout looks like:

• Phase 1: resolve the top 3 ticket types (tracking, return instructions, basic product questions)


• Phase 2: add tool actions (return labels, address change requests before fulfilment)


• Phase 3: add proactive updates (delay notifications, backorder messaging)


• Phase 4: extend to marketplace messaging and social inboxes

We also add sampling and QA. For example, you can review 20 random resolved tickets per day and score them. If quality dips, you adjust policy or temporarily reduce automation.

The end state is not zero humans. The end state is a small, strong team that handles exceptions, protects your brand, and improves the workflows, while the agent handles the repetitive work.

If your store is currently blocked by hiring cost, this shift is often the fastest way to grow without sacrificing customer experience.

Governance is part of scaling

As volume grows, you need a way to change policies safely. We set up a lightweight change process: update the policy source, run a small regression set (common ticket types), then ship. If something drifts, we can roll back.

We also keep a kill switch: a way to disable tool execution and fall back to draft-only mode. This is useful when an integration breaks or a carrier API changes.

A realistic staffing model

The most successful teams use the agent to keep the human team small and senior. Instead of hiring five junior agents for peak season, you keep one support lead and a small team that handles exceptions and improves workflows. The agent absorbs the repetitive load.