Brian Wisniach
Content Marketing
TL;DR
Agentic post-purchase means AI agents that take action on the work after checkout (support, reviews, resale, and risk) rather than just answering questions about it.
AI is taking over discovery and, increasingly, buying, but the merchant still owns everything after the order: the refund, the return, the dispute.
The right test for any post-purchase agent is whether it owns an outcome, not whether it can chat.
What is agentic post-purchase, and how does it relate to agentic commerce?
You’re most likely used to hearing agentic commerce described from the shopper’s side: an AI agent researches a product and buys it on someone’s behalf. Most explainers treat everything after the order as a footnote, which is a strange place to be vague, since that’s where the work lands on the merchant.
OpenAI’s own Agentic Commerce Protocol FAQ states that the merchant handles refunds and chargebacks, since the merchant accepts payment directly from the customer. And after OpenAI deprioritized Instant Checkout in March 2026, Storyboard18 reported that merchants can use their own checkout systems while ChatGPT focuses on product discovery. The AI gets the introduction, and the merchant gets the aftermath.
Buyer-side agents will also start contacting merchant support. Gartner’s 2025 press release expects customers to increasingly use AI agents to initiate, manage, and negotiate service requests for them, which means your support queue is about to have machines on both ends of it.
Our view is simple. AI is welcome to help shoppers discover. Agents should take over the legwork after the order. It helps to think in three stages:
A chatbot knows your return policy.
An agent can carry out a return, with someone watching.
An agency answers for how returns go, month after month.
Every moment in an order can be handled at any of these three stages, and the stage you pick decides who is on the hook when something goes wrong. The table below shows where each kind of agent fits across an order, and the sections after it show what each one does today and who is building it.
Where do AI agents fit in the post-purchase experience?
Follow a single order from the first product question to the day something comes back, and you’ll pass through five moments where an AI agent could be doing the work. The table below pairs each moment with the type of agent suited to it, what that agent actually does, and the number you’d judge it on.
Moment | Agent type | What the agent does | Measured on |
|---|---|---|---|
Before purchase | Shopping agent | Answers product questions, recommends, guides to checkout | Conversion rate, AOV |
Order issue | Support agent | Resolves WISMO, order edits, returns, refunds | Resolution rate, CSAT |
Delivery confirmed | Reviews and insights agent | Requests, moderates, and analyzes reviews | Review volume, verified review share |
Item returned or unwanted | Resale and liquidation agent | Lists, prices, sells, delists | Recovered value |
New order placed | Risk agent | Flags orders needing a closer look and explains why | Dispute rate, loss rate |
What separates these from the chatbot you already have is the last column. Resolution rate, recovered value, and dispute rate are results rather than activity counts, so every agent on the list has to keep earning its seat. A feature can be switched on and forgotten.
5 ways retailers can use AI agents across their e-commerce operations
AI agents are doing real work at five points in an order’s life, and the first starts before checkout, because a product question left unanswered there tends to come back later as a return or a ticket. Each section below covers how the work used to get done and what changes with an agent.
Pre-purchase product recommendations and conversion
Discovery is already moving off the site. Adobe’s holiday shopping data shows traffic to retail sites from generative AI tools rose 693.4% year over year between Nov. 1 and Dec. 31, 2025, which means more shoppers are arriving after an AI has already pointed them there. The old toolkit of site filters, product quizzes, and static “you may also like” widgets wasn’t built for that shopper. A shopping agent can take a request like “breathable but elegant outfit for a beach wedding,” read the catalog and the reviews, and walk the shopper to checkout. Gorgias AI Agent includes shopping assistant skills, Kodif works in this space, and Seel’s Sales Agency answers product questions and lets shoppers make purchases directly.
Post-purchase support
Most support teams already have some automation, usually a chatbot that points shoppers to an FAQ page or a macro that saves someone two minutes of typing. The refund, the address edit, and the return label still end up on a person’s desk. An AI agent changes that by working inside the helpdesk and the store system the way a team member would, so a return request gets checked against policy, refunded, and closed without anyone handling it by hand.
Gorgias, Intercom Fin, Seel’s Support Agency, and Zendesk all offer AI agents at this level. Seel built its agency on its own ticket queue before offering it to merchants, and reports 70 million tickets processed in its own operations and an 80%-plus resolution rate on Seel’s own tickets. For a closer look at how this works, see our guide to what agentic customer support is.
Reviews and customer insights
Review requests usually go out on a fixed timer after an order ships, which is really a guess about when the package arrives, and the reviews that come back get read when someone has time. AI can tie the request to confirmed delivery, follow up in the shopper’s own language, moderate what comes in, draft replies for a person to approve, and cluster the themes into product feedback instead of leaving a pile of star ratings. Okendo and Yotpo both build AI into review collection and analysis.
Inventory resale and liquidation
Resale comes in two flavors that tend to get lumped together: shopper resale, which helps a customer sell something they no longer want, and merchant liquidation, which routes returned and excess inventory to wherever it will recover the most value. Both used to depend on someone grading items by hand, and the default outcome was a bulk liquidator paying a steep discount. Agents decide the disposition (restock, resell, or liquidate), write the listing, price across marketplaces, and delist everywhere once an item sells. Beyond the recovered revenue, resale keeps the shopper in the relationship after the sale is supposedly done. Archive, Seel’s Resale Agency, ThredUp’s Resale-as-a-Service for branded resale, and Trove all work in this area.
Assessing risky orders
Order-risk review has mostly meant rules engines, manual queues, and fighting chargebacks after the fact. An agent can review new orders daily, flag the ones that deserve a closer look, explain why in plain language, and recommend an action, with the merchant making the final call. The explanation matters as much as the flag, because a flag nobody can interpret isn’t usable.
The numbers vary with what each source counts, but they show losses coming from more than one place:
The National Retail Federation and Happy Returns’ 2025 Retail Returns Landscape found that 9% of all returns are fraudulent.
Appriss Retail’s 2026 Total Retail Loss Benchmark Report puts $86 billion in losses on returns abuse, six times more than returns fraud.
In Chargebacks911’s 2026 Field Report, nearly three-quarters of merchants rated friendly fraud, where a customer disputes a legitimate purchase, a moderate or significant concern.
Riskified, Shopify’s Fraud Analysis with Flow automation, and Signifyd all operate in the order-risk space.
What should merchants look for before letting an AI agent act on their behalf?
Letting software act in your store is different from letting it answer in your chat window. Before turning one on, run it through these:
Does it take actions inside your systems, or only answer?
What outcome is it measured on, and who is accountable when it’s wrong?
Can it explain its decisions in plain language?
Is there a working path to a human? Berkeley’s California Management Review flags the lack of one as a major source of chatbot frustration.
Does it disclose that it’s AI where required?
Does it run alongside your team, or does it require replacing a vendor?
Seel’s Support Agency is built around that last question. It works inside the helpdesk a merchant already runs, starting with Zendesk, so adopting it doesn’t mean switching platforms, and the CX lead stays in the loop by reviewing and approving its proposed updates in a daily AI Manager review. It’s measured on resolution rate and CSAT rather than a feature list. If you’re mapping where agents belong in your own post-purchase stack, talk to sales and we’ll walk through it with you.
FAQ
Is agentic post-purchase the same as agentic commerce? No. Agentic commerce usually describes AI agents that research and buy on a shopper’s behalf. Agentic post-purchase covers what happens after the order: AI agents handling support, returns, reviews, resale, and risk. The two connect at checkout, where the merchant still owns refunds and chargebacks.
Will AI agents replace post-purchase support teams? Not the good ones. Agents take the repetitive, rules-based work such as order status, address changes, and standard returns, so people can focus on exceptions and judgment calls. The strongest deployments keep a human in the approval loop and a clear path to a person for shoppers who need one.
Do businesses have to tell customers they’re talking to an AI? It depends on where you operate. There’s no general federal rule in the US, but California’s BOT Act offers a safe harbor for clear disclosure, Utah requires it on request, and the EU AI Act’s transparency rules began applying in August 2026. Labeling your bot is the simple, safe default. This isn’t legal advice.
