Understand
Read the request and the records behind it before proposing a next step.
AI agents for business
Software that does the work you already repeat
We find the workflows worth automating, then build agents that use your systems, ask for approval when money moves, and run in production. eCommerce is where we go deepest. AWS is how they stay up.
- 64-part field guide
- Human on anything that moves money
- Checked on real tickets before launch
- Repeat work arrives.
- One runtime on Amazon Bedrock AgentCore.
- Five families of work: support, sales, operations, inventory, and knowledge.
- Writes that move money stop at a human approval gate.
- Approved work runs in production.
AWS Select
Tier Services Partner
64
Part Field Guide
Human
On anything that moves money
Checked
On real tickets before it goes live
Not a chatbot
A worker with tools — and a stop button
If it cannot use your systems, and cannot be stopped, it is a demo. Production looks like this.
Use tools
Call only the systems it is allowed to use. No open-ended access to “whatever looks useful.”
Ask for approval
Refunds, price changes, purchase orders, and anything else that moves money wait for a human.
What could you automate?
Five families of work
If the same tickets, reports, or decisions repeat every week, an agent can usually take the first pass. Each card opens the commercial page for that family. Build guides stay linked from there.
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.
Understand, use tools, then stop.Customer support agentSales & B2B
A B2B AI sales agent that prepares quotes, reorders, and account answers from customer-specific price — not the public catalog. Sales still owns the relationship.
Understand, use tools, then stop.Sales & B2B agentOperations
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.
Understand, use tools, then stop.Operations agentInventory
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.
Understand, use tools, then stop.Inventory agentKnowledge & catalog
If orders, customers, products, and inventory cannot be joined, an agent will guess. This is the data layer that makes every other agent honest.
Understand, use tools, then stop.Knowledge agentIf you have not built an agent yet
Nine executive notes
Short notes for someone who has not built an agent yet. They use published figures — McKinsey's 62% experimenting, a readiness score out of 30 — and they do not invent a ticket or sales result.
What business processes should you automate with AI?
Start with the lookup pile — status, policy, a reorder brief — not tax, capture, or labels. McKinsey 62% experimenting; 23% scaling.
Read the note →
How AI agents can reduce customer support work
Lookups for where-is-my-order and policy. That slice is about 18% of tickets. Refunds wait for a person.
Read the note →
AI agents vs chatbots
A chatbot searches help articles. An agent uses tools and can be stopped. Relabeling search as an agent is the expensive failure.
Read the note →
Where should a business start with AI agents?
Score readiness out of 30. Below 16, do not let it change orders. Then pick one family — usually support.
Read the note →
How to evaluate AI agent opportunities
Score how often it happens, how much it hurts, whether the data exists, and what a wrong write would do. About $791 a month at 50,000 sessions is a platform cost, not money saved.
Read the note →
How to show the board what an AI agent did
Sessions, handoffs, writes denied, and one wrong answer. About $791 a month at 50,000 sessions is a bill to plan, not money saved.
Read the note →
What should an inventory agent reorder?
A risk brief — reorder, wait, stockout, or excess. The 5-day cover figure is a fixture. A buyer still sends the purchase order.
Read the note →
What a B2B agent may quote
Contract price, or no number. The $5,000 gate in the field guide is a placeholder. A person still sends the quote.
Read the note →
Ten tickets before the agent talks to a customer
Ten lookups and three must-escalate cases, with a trace. Under 16 out of 30, do not add a write.
Read the note →
Where we go deepest
eCommerce is the published practice. AWS is the platform.
This hub is the commercial door. Agentic commerce is the two-halves offer for stores. The field guide is the library. They stay separate on purpose.
Agentic commerce
Agents you run inside the store, and agents that buy from your catalog. ACP, UCP, and a catalog that survives a model’s comparison.
Open agentic commerce
The engagement
One agent, scoped to one job, then the next. Built on AWS. You keep the setup and the runbooks. This is what you buy — not a separate product for each team.
eCommerce AI Agents on AWS
How we implement
One agent in production beats five in a slide deck
We ship in that order. If you are not ready, the readiness check says so.
Readiness
We check whether your data, systems, and approval paths can support an agent at all — the failure mode that kills most agent projects before the model is even chosen.
Scope one agent
We pick the single workflow with the clearest owner and the cleanest data, define its tool catalog and write boundaries, and agree the human approval gate before a line of code exists.
Ship with evals
Built on AWS, checked against real tickets, with a cost cap and a record of what it did. It goes live when it clears that bar — not when the sprint ends.
Expand the fleet
Add the next agent against the same guardrails, then the supervisor layer that coordinates them. Your team owns the IaC and the runbooks when we leave.
Guides, not this page rewritten
Where the long-form lives
This hub will not try to be the 64-part library. Use the surfaces below when you want depth.
Field guide
A 64-part published guide to building eCommerce agents on AWS, with copyable artifacts. That is the library.
Read the field guide
AI Agents category
Every related post, including parts outside these five families. This is a blog listing — not this hub, and not a redirect target.
Browse the category
Common questions
Before you start
Plain answers for operators who have not built an agent yet — including that we do not have agent case studies to sell you.
What is an AI agent, in business terms?
An AI agent is software that does a job a person already does — looking up an order, deciding what to reorder, drafting a B2B quote — by reading your systems, choosing the next step, and taking the action, with a human in the loop when money or customers are at risk. It is not a chatbot that only answers from a FAQ. If it cannot use your tools and cannot be stopped, it is not ready for production.
How is this different from a chatbot?
A chatbot replies. An agent acts: it looks up records, chooses a next step, and uses tools you have allowed. Chatbots fail when the answer is not in a script. Agents fail when they can write to systems they should not touch. That is why we put a tool catalog, write boundaries, and a human approval gate on anything that moves money — before the model is the conversation. The evaluation table is https://www.factualminds.com/compare/ai-agent-vs-chatbot/.
Which work should we automate first?
The workflow with a clear owner, clean data behind it, and a decision a human already makes the same way every time. In practice that is often customer support and WISMO. We publish the scoring method — volume, data readiness, blast radius if it is wrong, and effort — so you can run it before talking to us. If the data cannot be joined, start with the knowledge layer, not a flashy agent.
Do we need to know AWS — or already have AI — to start?
No. The first conversation is about the work: which tickets, reports, or decisions repeat every week, who owns them, and what happens if the answer is wrong. AWS matters because that is where production agents (and the rest of your stack) usually run, and we are an AWS Select Tier Services Partner. You do not need to speak Bedrock to get a useful recommendation.
Do you have case studies of AI agents in production?
Not yet, and we would rather say so than dress up something adjacent. What is public today is a multi-part field guide with copyable artifacts, free readiness and pricing tools, and AWS delivery work (security, GenAI, HIPAA) that is not the same as a production commerce agent. Judge the thinking on work you can read. We will not invent a customer story to close a call.
What is the difference between this hub, Agentic Commerce, and the field guide?
This hub is the commercial door: which work an agent can take on, how we implement, and how to start. Agentic Commerce is the eCommerce offer — agents you run plus agents that buy from your catalog. The field guide is the library: part-by-part build notes. The blog category “AI Agents” is every related post. None of those URLs redirects to another. The engagement you buy is still eCommerce AI Agents on AWS.
How do you stop an agent from doing something expensive or wrong?
Four controls, in this order. A tool catalog that defines exactly which actions the agent can take. Authorization on every write, so mutating an order or a price is a permission decision rather than a prompt decision. A human approval gate on anything that moves money. And an eval suite with a golden dataset and a pass bar the agent has to clear before it goes live.
Find your first AI agent opportunity
Tell us the repetitive work that is eating the week. We will say whether an agent is the right move — and if it is not, we will say that too.