Pick one workflow join
Usually WISMO: platform order id to OMS id. Document it. Do not boil the lakehouse first.
Knowledge and catalog
Knowledge Agent
If orders, customers, products, and inventory cannot be joined, an agent will guess. This is the contract that stops the guess — not a chatbot over your lakehouse.
Last updated: August 31, 2026
The work today
Teams buy an agent runtime, then discover Shopify order ids, OMS ids, and WMS ids disagree; inventory is a nightly dump with no as-of; guest and account records duplicate the same shopper. The model treats a guessed join as a fact. A pretty dashboard is not a data layer. A SQL copilot on the warehouse is the anti-pattern.
What the agent does
A knowledge agent — and the data work underneath it — exposes named read tools over a join contract: orders, customers, products, inventory, returns, and support, with as-of on anything that moves. It answers "can these records be the same thing?" and flags quality breaks. It does not browse the web as a generic researcher, and it does not get unrestricted SQL.
Workflow
How a turn runs
Reads, tools, then a stop. Skip the stop and you have a demo.
Name the tools
getOrder, getInventoryPosition, getCustomer — each with the keys it accepts. Reject rows that cannot join.
Stamp as-of
Inventory and price without a timestamp are how afternoon agents lie. Quality monitors watch the contract, not a vibe.
Then host an agent
Runtime comes after the join works. An agent on three identities for one order will invent causality in support, returns, and RCA.
Systems
What it is allowed to touch
Named tools only. Anything undeclared is unreachable, regardless of the prompt.
Orders, customers, products, inventory
The four domains every other family needs. Returns and support tickets join here or they stay unknown.
Catalog contract
Parent/child, identifiers, required attributes, checkout-true offer. Shopping agents and internal tools consume the same SKU truth.
Quality monitors
Break the join, fail the eval. Do not let the model fill a missing campaign id with a channel story.
Approval
Knowledge tools are reads. Writes to master data (merge customers, edit a SKU, overwrite ATP) are human-approved or they do not exist. PII leaves the payload unless identity policy allows it.
What good looks like
Outcomes we will stand behind
Qualitative on purpose. We do not have published agent case studies, so we will not invent a percentage.
Other agents can tell the truth
Support, inventory, and sales stop inventing joins. That is the outcome. It is not a research assistant for the exec team.
A contract you can test
Named keys, as-of stamps, and evals that fail when a tool returns two identities for one order.
Catalog and ops share one picture
Inbound shopping agents and internal tools read the same product and inventory facts. Fix the attributes once.
This page is the commercial summary for the knowledge and catalog family. It is not a generic research agent, not a chatbot over a data lake, and not a rewrite of the data-layer field-guide post.
Catalog vs business data
The AI-ready product catalog is the SKU contract shopping agents consume. This family is the business join: orders, customers, products, inventory, returns, support. If SKUs fail the catalog contract, fix that first. If an order cannot find its customer, stay here.
What this is not
/ai-agents/research/or/ai-agents/finance/— those URLs are not in v1.- Unrestricted SQL with a chat window.
- Amazon Bedrock as “the data layer.” Bedrock is the model. The layer is your keys, your tools, and your as-of stamps.
CTOs who want the runtime next can go to Bedrock AgentCore and the agentic commerce pattern. Operators who just need WISMO to tell the truth should stay on this page, then customer support.
How we start
One workflow. One join. Named reads. Then the eCommerce AI Agents engagement — or a readiness pass if the keys are not there yet.
Field-guide depth
These posts are the long-form canonicals. This page does not replace them, and they are not redirected here.
Why Your eCommerce Data Is Not Ready for AI Agents (2026)
AI-ready eCommerce data is a seven-domain join contract with asOf — not a warehouse copilot. Reuse Gateway ~180→95 ms and ~$791/mo at 50K sessions; those are not store KPIs.
The AI Data Agent: Connecting Orders, Customers, Products and Inventory (2026)
A data agent joins orders, customers, products, and inventory through six named tools — no unrestricted SQL. Reuse Gateway ~180→95 ms and ~$791/mo at 50K sessions; not store KPIs.
AI Product Catalog Management for Large eCommerce Catalogs: Extract, Validate, Then Publish (2026)
AI product catalog management drafts attributes and copy; deterministic gates block publish. Reuse Gateway ~180→95 ms and ~$791/mo at 50K — sample rules, not a client SKU KPI.
AI Agent vs Workflow Automation: What Should eCommerce Businesses Use? (2026)
AI agents do not replace Shopify Flow or OMS state machines. Hybrid is the default: rules own money movement; the agent returns a structured decision. Reuse Gateway ~180 to 95 ms and ~$791/mo at 50K sessions — not store conversion KPIs.
Human-in-the-Loop AI Agents for eCommerce: Where Automation Should Stop (2026)
HITL is a queue with session id and tool trace — not a prompt. Reuse Gateway ~180 to 95 ms and ~$791/mo at 50K sessions. Do not auto-approve on SLA timeout.
The eCommerce AI Agent Readiness Assessment: Is Your Business Ready? (2026)
Score org-wide AI agent readiness /30 — data, integration, process, governance, priority. Below 16, skip writes. Reuse Gateway ~180→95 ms and ~$791/mo at 50K sessions.
The engagement
eCommerce AI Agents on AWS
Production AI agents for eCommerce on Amazon Bedrock AgentCore — support and WISMO, inventory, merchandising, margin, returns and B2B. Tool boundaries, evals before launch, and a human on anything that moves money.
Amazon Bedrock AgentCore Consulting
Production agent infrastructure on Amazon Bedrock AgentCore — Runtime, Gateway, Memory, Identity, Observability and Evaluations. Bring your own framework; we operate the substrate.
Where this sits
Family page → industry or decide tree → the engagement. AWS cases on this site are not agent results.
Other families
Five kinds of work
Customer support
The same WISMO, tracking, and returns questions every day. An agent looks up the order, answers from live data, and hands off the moment it is out of its depth.
Customer support agent →
Sales & B2B
Quote requests, reorders, and account questions from buyers who will not tolerate a chatbot. An agent drafts the next step; a human still owns the relationship.
Sales & B2B agent →
Operations
The morning pile: exceptions, stalled orders, and the work nobody wants to triage. An agent ranks what needs a human today — it does not silently rewrite the warehouse.
Operations agent →
Inventory
What to reorder today, what is about to stock out, and what has quietly become dead stock — answered against live data, not last month's report.
Inventory agent →
Common questions
Before you start
Is this a research agent?
No. We did not create /ai-agents/research/. This family is catalog quality, join keys, and named read tools for commerce data. A generic web-research agent is out of v1 scope.
Do we need a lakehouse before the first agent?
No. Pick one workflow, document one join, expose named read tools, stamp as-of on inventory and price. A warehouse copilot is the thing we are trying to avoid.
Should we start with AgentCore or with the data layer?
Join keys first. Hosting a loop that calls three tools returning three identities for one order spends platform money on a demo that cannot name a canonical order id.
Do you have knowledge-agent case studies?
Not yet. The data-layer, data-agent, and catalog-management field-guide posts are the published depth. AWS analytics cases on this site are not this product.
Check whether your data can support an agent
Tell us which records you need to join for the first workflow. If they cannot be joined, we will say so before anyone picks a model.