Qore as Agentic Commerce for Amazon and Walmart
Operators asking which AI agents can run an Amazon or Walmart account deserve an answer that names the limits. Qore from Trellis is an agentic platform: it plans multi-step work and acts on it, inside guardrails you set and approval gates you control.
Most of the noise around agentic commerce describes shoppers handing purchases to assistants. The version that matters to a seller is the operator side: which parts of running a marketplace account can software carry from analysis through action, and which parts still need your sign-off. Ads, pricing, and reporting on Amazon and Walmart are repetitive, rule-heavy, and expensive to get wrong, which makes them the natural first place for agents to work.
Quick answer: agentic commerce is software that plans and acts, and Qore does it inside limits you set
- What it means: an agent takes a goal, breaks it into steps, reads the account data it needs, acts in the marketplace tools, and reports back.
- How Qore does it: by turning a workflow you describe into a visible, editable skill.
- Once locked, Qore runs the skill on a schedule, and any action that spends money or changes a live campaign goes through the approval settings you chose.
- Approval is the default. Autonomy is something you grant, action by action, once you trust the logic.
- The upside and the risk: work that runs without you watching it, against an agent acting on rules you never wrote down, which is why the limits section below matters as much as the use cases.
Agentic execution means plan, act, check, and report, so guardrails matter more than the chat window
What an agent does that a chat assistant does not
A chat assistant answers a question about your data. An agent completes a piece of work. The difference shows up in four steps:
- Plan. Break the goal into ordered steps (pull pacing by campaign, compare against thresholds, decide which campaigns need a change).
- Act. Take the step in the system, such as staging a bid change, pausing a target, or moving budget.
- Check. Compare the result against the rule that triggered it.
- Report. Put the outcome where your team already works: Slack, email, or a spreadsheet.
Every one of those steps is a place where an unbounded agent can do damage quietly. That is why the guardrail design decides whether an agent is usable on a live account.
Why chat-only and single-channel agents stop short
Amazon's Ads Agent and the Amazon Ads MCP Server (open beta since February 2, 2026) let assistants query and change advertising accounts. Both see advertising data only, with no view of inventory, margin, or Buy Box status. Our plain-English guide to MCP for Amazon sellers covers what that connection does and does not give you, and Can Claude Manage Amazon Ads? covers the general-purpose chat case.
An agent that cannot see stock depth will happily push spend on a listing that is about to go out of stock. A conversational layer is useful for questions. Running the account needs a platform that holds the data and the rules together.
Qore plans by building a workflow you can inspect, then acts on a schedule
Q builds the skill, you lock it, Qore runs it
Qore is where you turn a review you do by hand into a workflow that runs itself. You describe the job to Q, the assistant that helps you build and shape workflows. Q assembles the steps as a visible graph, showing which data feeds which decision. You edit it, lock it, and Qore runs that same logic each time, on a schedule or on demand. Trellis calls these workflows skills, and you can template one across a roster of accounts.
The planning happens in the open, before anything runs. You read the plan, change a threshold, and approve the logic once, instead of hoping a model reasons its way to the same answer each morning.
Guardrails decide what runs alone, what waits, and what never happens
You configure which actions require approval and which can run automatically. A workable starting map looks like this:
| Action tier | Examples | Default handling |
|---|---|---|
| Read and report | Pacing checks, audit findings, weekly summaries | Runs on schedule, posts to Slack, email, or a sheet |
| Reversible, low-risk changes | Negating clear waste terms, pausing a dead target | Approval first; can move to automatic once you trust the rule |
| Anything that spends money | Bid increases, budget moves, campaign launches | Approval required unless you deliberately set it otherwise |
| Strategy calls | Which products to push, margin targets, launch timing | Stays with you; Q helps you shape the standard, it does not set it |
Concrete use cases show where agentic work pays off on Amazon and Walmart
Qore works across Amazon and Walmart, so a skill you build for one channel's routine has a counterpart on the other. The strongest first candidates are the jobs your team already runs from an SOP.
- Budget pacing. Instead of a person re-running the same checks each morning, the SOP becomes an always-on process. Pacing is monitored across campaigns, your thresholds and decision matrix are applied, and recommended budget or bid changes are routed to a person for approval. The full workflow is in our Amazon Ads budget pacing SOP.
- Account audits. A scheduled review that flags wasted spend, structure problems, and coverage gaps before you scale. See the Amazon PPC account audit workflow.
- Recurring reporting. A weekly summary of what changed, where the opportunities are, and what to do next, delivered to Slack or email instead of built by hand each Monday.
- Exception handling. A monitor that watches for the conditions you define and puts the decision in front of the right person, with the evidence attached.
The pattern is the same each time. Codify the check you already trust, let Qore run it, and keep the judgment call with the person who owns the account. For the broader argument on why workflows beat one-off prompting, read AI for Amazon Ads.
Case material: an agency running Amazon and Walmart scaled without scaling headcount
Sequence Commerce, an Amazon and Walmart marketing agency serving brands including Pillsbury, Duracell, and ANS Performance, used Trellis automation for advertising, content optimization, and dynamic pricing. The published results across the portfolio were 259% aggregate sales growth, 14.8% margin growth, and a 5% reduction in TACoS. The agency's Head of Growth, Trevon Heath, said: "Having gone through a few advertising automation platforms before, I can see the attention Trellis has spent understanding and serving agency needs."
Read it as evidence for the operating model, not as a Qore-only result. These figures come from the broader Trellis platform, and results vary by account. The full write-up is in the Sequence Commerce case study.
How the options compare: chat assistants, native agents, and Qore
This table compares categories of tools, not vendors. Each row states what the category does well and where it gives ground.
| Capability | General chat assistant with MCP | Marketplace-native agent | Qore |
|---|---|---|---|
| Answers an open-ended, one-off question | Strong | Strong inside its own channel | Works, but a chat assistant is quicker for a novel question |
| Runs the same logic every time | Varies with the prompt and model | Depends on the product | Locked skills run the logic you approved |
| Can read inventory, margin, and Buy Box alongside ads once those sources are connected | Only what you connect | Advertising data only | Ads, pricing, and inventory signals on one data layer |
| Logic you can inspect before it runs | Limited | Limited | Visible workflow graph you edit and lock |
| Configurable approval gates on spend | You build them yourself | Varies | Built in; approval is the default |
| Setup effort | Low | Low | Higher: you codify the SOP first |
Many teams will use more than one. A chat assistant for exploration and Qore for the recurring work you want done the same way each time is a reasonable pairing.
Where the model runs out: what Qore does not decide for you
- Approval is a default you can change, and it costs speed. Every gated action waits for a person. That is the point, and it is also a trade-off on slow days.
- Strategy stays yours. Q helps you shape a standard and codify it. It will not choose your margin target or your launch plan.
- It works on the data it is connected to. Seeing inventory, margin, or Buy Box alongside ads is real, but it depends on wiring those sources in first; the plumbing has to be set up before an agent can act on them, and a skill is only as good as the accounts and signals connected behind it.
- Codifying takes thought. If your SOP lives in one person's head, writing it down is the first job, and it is worth doing anyway.
- Product stage matters. Confirm current availability and feature scope in a walkthrough before you plan around a specific action type.
Conclusion: start with one SOP, gate the spend, and let the agent earn autonomy
Pick the routine check your team resents most, such as pacing or the weekly audit. Describe it to Q, read the plan it builds, lock it, and keep approval on every action that spends money. Loosen a gate only after the skill has been right long enough that you would stop checking it anyway. If you want to see how that looks on your own accounts, book a walkthrough of Qore.
Frequently Asked Questions
Agentic commerce on the seller side means software agents that plan multi-step work and act on it in your marketplace accounts, such as monitoring ad pacing, staging bid changes, or building reports, rather than only answering questions.
They can audit accounts, monitor pacing, flag exceptions, prepare bid and budget changes, and deliver recurring reports. What they should not do without your say-so is spend money or set strategy.
Qore is where you turn a review you do by hand into a workflow that runs itself. Q, its assistant, helps you build the workflow as a visible graph. You lock it, and Qore runs it on a schedule with your approval settings applied.
Actions default to manual approval. You choose which actions require sign-off and which can run automatically, and you can grant more autonomy once you trust a skill.
Qore works across Amazon and Walmart, and skills follow the same build, lock, and run pattern. Confirm the specific Walmart data and actions available to your account in a walkthrough.
The Amazon Ads Agent and the Ads MCP Server see advertising data only, and chat assistants vary with the prompt. Qore holds ads, pricing, and inventory signals on one data layer and runs logic you inspected and locked.
No. It carries the repeatable checks and routine recommendations so people spend time on judgment. Strategy, priorities, and the approval decision stay with your team.