Agentic Commerce
Agents that run your store.
Agents that buy from it.
Two halves of one shift. We build both on AWS — AgentCore runtime, tool boundaries on every write, Cedar authorization, evals before launch, and cost caps that still hold on Black Friday.
The Shift
Two halves, and most stores are only working on one
The agents you deploy and the agents that shop your catalog are different problems with different failure modes. Both are already here.
Agents you run
Support and WISMO, inventory and reorder, merchandising and margin, returns and fraud, B2B quoting — plus the supervisor layer that keeps them from fighting over the same order. Each agent scoped to one job, with a tool catalog that defines exactly what it may touch.
Agents that buy from you
Shopping agents inside ChatGPT, Gemini and elsewhere now read catalogs, compare products, and complete checkouts. Two open standards have emerged — the Agentic Commerce Protocol from OpenAI and Stripe, and the Universal Commerce Protocol led by Google with Shopify, Etsy and Walmart. Your product data has to survive their comparison.
Agent Families
Eight families, 64 published build guides
Every family below has a full write-up behind it: architecture, tool catalog, the human approval gate, and what breaks when you skip one.
Store Operations
The workflows most merchants automate first: support, WISMO, back office, and daily priorities.
Customer and Revenue Agents
Recommendation, recovery, retention, and post-purchase — with discounts and writes gated behind a human.
Inventory, Purchasing, and Vendors
Reorder briefs, dead stock, demand signals, and PO approval gates. Humans still sign the purchase order.
B2B Commerce Agents
Contract pricing, quotes, reorder cadence, and onboarding — where a shopper JWT must never read a buyer price.
Agentic Commerce and AI Discovery
What changes when the buyer, or the thing choosing the SKU, is somebody else's model.
Autonomy, Governance, and Readiness
How much autonomy per action, where the human sits, and what to fix before the model.
Data, Margin, and Proactive Operations
Join keys, evidence-backed alerts, and agents that raise the exception before anyone opens a dashboard.
Multi-Agent Architecture and Integration
When one agent stops being enough, what it should remember, and how it reaches Shopify, ERP, CRM, and WMS.
What we build before we build the agent
A demo is a prompt. Production is everything around it.
The model is rarely what fails. 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. We build the unglamorous parts first, because they are the parts that decide whether the agent is still running in ninety days.A tool catalog, not an open connection
Every action the agent can take is declared, typed, and reviewable. Anything not in the catalog is not reachable.
Cedar authorization on every write
Mutating an order, a price, or a customer record is a permission decision evaluated outside the model — not a prompt the model can be talked out of.
A human on anything that moves money
Refunds, price changes, and purchase orders route to approval by design. The agent assembles the evidence; a person makes the call.
Evals with a pass bar, before launch
A golden dataset per agent and a threshold it must clear. If it does not clear, it does not ship — the sprint calendar does not get a vote.
Cost ceilings that hold under load
Token budgets and per-conversation caps, so a traffic spike arrives as a scaling event rather than an inference bill.
Agent-Ready Store
Making your catalog legible to somebody else's model
The inbound half. Less understood, moving faster, and largely unaddressed by the platforms most merchants are standing on.
Protocol surfaces — ACP and UCP
The Agentic Commerce Protocol (OpenAI and Stripe, live in ChatGPT since September 2025) and the Universal Commerce Protocol (Google-led, launched early 2026 with Shopify, Etsy and Walmart) define how an agent discovers, compares and buys. We assess which one your channels need and build the surface behind it.
An MCP server for your catalog
Model Context Protocol is how agents reach your product, inventory and order data without you shipping a bespoke integration per assistant. Hosted on AWS, scoped to reads you actually want exposed.
Product data that survives comparison
Agents compare on attributes, not adjectives. Missing dimensions, unstructured variants, and marketing copy where a spec should be are how a product quietly stops being recommended.
Discovery and generative engine optimization
Staying visible when the thing choosing the SKU is a model rather than a shopper scrolling a results page.
How We Work
From readiness check to a fleet that runs itself
One agent in production beats five in a slide deck. We ship in that order.
Readiness
We check whether your data, systems, and approval paths can support an agent at all. Missing join keys between orders, customers, products and inventory is the failure that stops most eCommerce AI projects before a model is even chosen.
Scope One Agent
We pick the single 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 Bedrock 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
Add the next agent against the same guardrails, then the supervisor layer that coordinates them. Your team owns the infrastructure-as-code and the runbooks when we leave.
Engagements
Three ways this gets built
Fixed-scope where scope creep does the most damage; retained where the work is genuinely ongoing.
eCommerce AI Agents on AWS
The agents you run. Support and WISMO, inventory, merchandising, margin, returns and B2B — scoped one at a time, each with a tool catalog, an approval gate and an eval pass bar.
Agentic Commerce Readiness
The agents that buy from you. ACP and UCP surface assessment, an MCP server over your catalog, and the attribute work that decides whether an agent shortlists your product.
Amazon Bedrock AgentCore
The substrate underneath both. Runtime, Gateway, Memory, Identity, Observability and Evaluations — designed, deployed and handed over as infrastructure-as-code you own.
Model It First
Run the numbers before you run a proof of concept
Free, no email gate. Both tools are the same ones we use on the first call.
Agentic Commerce Readiness Checker
Score your store on the five things that decide whether an AI agent can work for you — or sell for you. Data joins, catalog legibility, systems access, approval paths, and peak-season discipline.
eCommerce AI Agent ROI Calculator
Model hours recaptured, labour cost avoided, agent run cost and payback for one candidate workflow — before you commission a proof of concept.
Bedrock AgentCore Pricing Calculator
Estimate AgentCore Runtime, Gateway, Memory, Browser, and Code Interpreter platform fees — separate from Bedrock model inference tokens.
GenAI Readiness Checker
Measure data, team, and infra readiness — get a recommended AWS AI starting point (Bedrock, SageMaker, or Amazon Q).
Common Questions
Agentic Commerce — Frequently Asked Questions
What is the difference between an AI agent and the workflow automation we already have?
What are ACP and UCP, and do we need both?
How much autonomy should an eCommerce agent actually have?
Why Amazon Bedrock AgentCore rather than a framework?
Our data is a mess. Is that a blocker?
Can you work alongside our engineering team rather than replacing it?
Do you have case studies of eCommerce agents in production?
How does an AWS partnership help with something that is mostly a model problem?
Ready to put an agent in production?
Start with a readiness check. We will tell you which agent to build first — and which one you are not ready for yet.