Agentic Commerce
Support and WISMO, inventory and reorder, merchandising and margin, returns and fraud, B2B quoting — built on Amazon Bedrock AgentCore with a declared tool catalog, Cedar authorization on every write, and a human on anything that moves money.
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An agent is not a chatbot with your catalog pasted into the system prompt, and it is not a workflow you drew in advance. Workflow automation follows a path you defined: if this, then that. An agent is given a goal, a set of tools, and permission to decide which tools to call and in what order.
That flexibility is the entire value and the entire risk. It means the agent handles the case you did not anticipate — the customer whose order shipped in two parcels, one of which was scanned as delivered to a neighbour. It also means the agent can take an action you did not anticipate. Which is why, on every build we ship, the tool catalog and the write boundary get designed before the prompt does.
We have written this up in depth. The eCommerce AI Agents field guide is a 64-part series covering each agent family below, with the architecture, the tool catalog, the human approval gate, and what breaks when you skip one.
The temptation is to start with the most impressive agent. The right move is to start with the one that has a clear owner, clean data, and a decision a human already makes consistently.
In practice that ranking usually looks like this:
The AI Agent ROI guide publishes the scoring method we use — volume, data readiness, blast radius, effort — so you can run the exercise on your own workflows before engaging anyone.
If the next question is money, the cost worksheet splits platform, model, integration, and reviewers. The vendor checklist and the RFP template are what you send a bidder. None of them is a case study. We still do not have one.
Most eCommerce AI projects fail before a model is involved. The readiness assessment exists to find that early rather than in month three.
An agent asked which SKUs to reorder needs orders, inventory, products and vendors to join reliably. If those systems disagree about what a product identifier is, the agent will produce confident, wrong answers — and confident wrong answers are worse than no agent, because someone will act on them.
This is why the readiness assessment comes first and why, for a meaningful share of merchants, the honest first engagement is a data-layer project rather than an AI one. We would rather tell you that in week one than in month four. See why your eCommerce data is not ready for AI agents for the specific join keys and contracts involved.
Everything above is outbound — agents you operate, taking actions in your systems. There is a second half to agentic commerce, and it is moving fast: shopping agents inside ChatGPT, Gemini and elsewhere now read catalogs, compare products and complete checkouts on a customer’s behalf.
Two open standards have emerged. The Agentic Commerce Protocol, created by OpenAI with Stripe, has been live in ChatGPT since September 2025 with partners including Instacart, DoorDash, Shopify and Etsy. The Universal Commerce Protocol, led by Google with Shopify, Etsy and Walmart, launched in early 2026.
Making your catalog legible to those agents is a different engagement — see Agentic Commerce Readiness — but it draws on the same underlying work: structured product attributes, reliable join keys, and a callable surface over your commerce data.
The model is rarely what fails in production. What fails is an agent with unbounded write access, no way to measure whether it is right, and no ceiling on what it costs when traffic triples.
Commerce makes that harder than most domains. Agents run beside a PCI-scoped checkout they must never wander into, against data spread across a storefront, an ERP, a CRM and a warehouse system, during the one week of the year when an hour of degradation is a number the board sees.
That is the half of the work we have been doing since before agents existed. FactualMinds is an AWS Select Tier Services Partner with AWS-validated Foundational practices in RDS Solution and Delivery, CloudFront-Powered CDN migration, and Static Image Delivery using S3 and CloudFront — and we have run Amazon SES at 200M+ messages a month for an eCommerce platform.
Readiness. Fixed scope. We assess whether your data, systems and approval paths can support an agent at all, and produce a ranked list of candidate workflows with the blockers named.
Scope one agent. We pick the workflow with the clearest return, define its tool catalog and write boundaries, and agree the human approval gate before any code exists.
Ship with evals. Built on AgentCore with a golden dataset, a pass bar, cost caps and an audit trail. It goes live when it clears the bar.
Expand the fleet. The next agent runs against the same guardrails, then the supervisor layer that coordinates them. Your team owns the infrastructure-as-code and the runbooks throughout.
Order lookup, tracking, delivery exceptions and returns handled end to end, with a hard escalation path the moment the agent leaves its competence. The highest-volume, lowest-blast-radius place to start — which is why it is where most merchants should.
What to reorder today, what is about to stock out, what has quietly become dead stock, and which vendor is slipping on lead times — answered against live data with the evidence attached, not against last month's report.
Pricing intelligence, margin analysis and dynamic merchandising that surface the SKU losing money before the monthly close does. Price changes route to human approval by design.
Refund investigation and return-abuse detection that assemble the evidence — order history, delivery scans, prior claims — and hand a human the decision rather than guessing at it.
Quote requests, reorders, account management and onboarding for buyers who do not shop like consumers, will not tolerate a chatbot, and expect their contract pricing to be honoured.
When one agent is not enough: a supervisor, shared memory with per-namespace access control, and explicit ownership per action so two agents never fight over the same order.
We have published a 64-part guide covering every agent family above — architecture, tool catalogs, approval gates, and what breaks when you skip one — with copyable artifacts. Read the thinking before you buy the engagement.
Every action an agent can take is declared, typed and reviewable. Anything outside the catalog is unreachable. Cedar authorization evaluates every write outside the model, so mutating an order is a permission decision the model cannot be talked out of.
A golden dataset per agent and a threshold it must clear before launch. If it does not clear, it does not ship — the sprint calendar does not get a vote. AgentCore Evaluations runs it on every deployment.
Per-conversation token budgets, model routing by task, and CloudWatch alarms that fire before thresholds rather than after. A traffic spike should arrive as a scaling event, not an inference bill.
Agents in commerce run beside a PCI-scoped checkout, on data spread across four systems, during the one week of the year that matters. We are an AWS Select Tier Services Partner with AWS-validated practices in RDS, CloudFront CDN migration, and S3 and CloudFront image delivery.
Infrastructure-as-code, tool catalogs, eval suites and runbooks are yours, in your accounts, with your engineers in the pull requests throughout. No proprietary orchestration layer to be locked into.
Implementation guides for this service from our team of AWS experts.
Automate the lookup pile first — not tax, capture, or labels. McKinsey found 62% experimenting with agents and 23% scaling in at least one function.
Take the lookup work off the queue. Where-is-my-order is about 18% of tickets. Refunds stay with a person. We are not publishing a tickets-down percentage.
A chatbot searches help articles. An agent uses tools and can be stopped. Relabeling search as an agent is how Browser-hot pilots hit ~3× Runtime-shaped spend.
Start with readiness out of 30 — below 16 out of 30, do not let it change orders — then one family, usually support reads. McKinsey found 62% experimenting and 23% scaling.
Score volume, pain, data, and write-risk. Readiness below 16 out of 30 means do not fund writes. McKinsey found 62% experimenting and 23% scaling in at least one function.
Score org-wide readiness out of 30. Below 16 out of 30, do not let it change orders. McKinsey found 62% experimenting and 23% scaling — still a minority per function.
Fifteen store automations scored as traditional vs agent — not 15 week-one builds. Where-is-my-order is about 18% of tickets. Pick one read-heavy workflow this week.
Third-party tools we frequently wire into AWS as part of this engagement — production-tested integration guides for each.
Connect Shopify to AWS for AI agents and analytics — EventBridge webhook delivery, Admin GraphQL API, idempotent order processing, and the join keys agents need to be useful rather than dangerous.
Bring NetSuite cost, vendor and fulfilment data into AWS — SuiteQL and SuiteTalk REST, token-based auth, governance limits, and the landed-cost join that margin agents depend on.
Run LangChain and LangGraph on Amazon Bedrock and AgentCore — where the framework earns its keep, where a plain control loop wins, and how to keep agent code debuggable at 3am.
Architecture patterns, decision trees, and glossary terms that map to this engagement.
Reference architecture for eCommerce AI agents on AWS: AgentCore Runtime behind a Gateway tool boundary, Cedar authorization on writes, human approval on money-moving actions, and an MCP server exposing the catalog to inbound shopping agents.
Rank your first agent by data readiness, blast radius and volume — not by how impressive it sounds. Five questions, an opinionated recommendation, and the honest answer when you are not ready.
Agentic commerce is retail where AI agents act on both sides of the transaction — running store operations, and shopping on a buyer behalf through open protocols like ACP and UCP.
An AI agent is a system given a goal, a set of tools, and latitude to decide which tools to call and in what order — distinct from a workflow, which follows a path defined in advance.
A tool catalog is the declared, typed set of actions an AI agent may take. Anything not in the catalog is unreachable — making it the primary containment boundary in agent design.
Human in the loop is a design pattern where an agent assembles evidence and proposes an action, but a person approves it — applied per action, not per agent.
Agent evals are an automated test suite for agent behaviour — a golden dataset of cases with expected outcomes and a pass bar the agent must clear before deployment.
WISMO is the order-status enquiry — typically the single highest-volume contact type in eCommerce support, and the most common first workload for a customer-facing AI agent.
In-depth guides and best practices from our certified AWS architects.
On October 13, 2026 the Strands Agents team published about 94,000 Harbor trials. On matched Anthropic runs, estimated cost ran from about $0.74 to $1.57 per trial.
Read articleChecked 15 October 2026. The shared MCP adapter exposes 3 tools. execute-ability can run all 7 canonical abilities, including product-delete. Week one allows two reads.
Read articleOpenAI's 11 February 2026 Codex experiment merged about 1,500 pull requests. AgentCore Harness, GA 17 June 2026, still defaults to 75 iterations and a 3,600-second timeout — the harness sets those bounds, not the model.
Read articleIn-depth comparisons to help you choose the right approach before engaging.
Chatbot = search. Agent = tools plus a stop button. Pick search when the answer is already published. Pick an agent when it must use your systems. Relabeling is the expensive failure.
Buy search and SaaS widgets. Build (or partner-build) a governed agent when it must use your store systems and stop before money moves. Not a FactualMinds-always-wins page.
Agentic Commerce Protocol (OpenAI/Stripe) vs Universal Commerce Protocol (Google) — scope, surfaces, merchant effort, and which one to implement first.
Practical comparison of AWS Bedrock vs SageMaker for CTOs and ML architects. Evaluate generative AI platforms for your use case.
We will tell you which agent to build first, what your data needs before it can support one, and which agent you are not ready for yet.
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