Would it change prices automatically?

Only if you want it to, and within hard rules — margin floors, brand restrictions, change frequency caps. Most merchants start with proposals requiring approval and relax that once they trust the behaviour.

How is this different from a rules-based repricer?

A repricer applies a formula. An agent investigates why something changed — a competitor stockout, a supplier price move, a shift in your own sell-through — and factors that in. Where a simple rule suffices, we will tell you to use the rule.

How do you stop an agent doing something wrong?

Two mechanisms. First, permissions: an agent can only call the tools you granted it, and write actions are scoped as narrowly as the job allows. Second, approval gates on anything irreversible — sending external email, moving money, deleting records — so a person confirms before it happens.

How is this different from workflow automation?

Automation follows a path you defined in advance. An agent chooses the path at runtime. If you can draw the process as a flowchart that does not need constant new branches, automation is cheaper, faster, and more predictable — we will tell you when that is the better fit.

Autonomous AI Agents · E-commerce

Autonomous AI Agents for E-commerce

Merchandising decisions are made from data nobody has time to read.

Pricing, stock, supplier, and competitor data all move daily, and acting on them requires cross-referencing several systems. Agents monitor continuously, investigate anomalies across sources, and propose actions with reasoning attached — leaving the decision with you.

Get a Free Assessment See Proof

Step reasoning: MultiStep reasoning
Human approval: GatedHuman approval
Every action: TracedEvery action

Typical stack

LangGraph
OpenAI
Anthropic
FastAPI
Postgres

Use cases

What this handles for e-commerce brands

Monitor competitor pricing and propose adjustments within your margin rules
Investigate stock anomalies across supplier, warehouse, and sales data
Draft product content from supplier specifications for review before publish
Triage complex support cases that span order, payment, and shipping systems

Continuous merchandising analysis with recommendations you approve rather than reports you read.

Context

The e-commerce problem underneath it

E-commerce volume scales faster than the team handling it. Support tickets, returns, supplier updates, product content, and channel reconciliation all grow linearly with orders — and all of them are largely mechanical. AI systems absorb that growth without proportional hiring.

The same 'where is my order' question dominates support volume around the clock
Returns and exchanges involve several systems and a lot of copy-paste
Product descriptions and attributes are a bottleneck on every new range
Inventory and pricing live in different places across marketplaces and your store

What we build

Inside an autonomous AI agent

We scope agents narrowly and give them explicit permissions. An agent that can read from five systems and write to one is dramatically safer than a general-purpose assistant with broad access. Every action is traced, every decision is inspectable after the fact, and anything irreversible waits for human sign-off.

Planner-executor architectures that decompose a goal into verifiable steps
Tool-use layers with typed inputs and strict permission boundaries per tool
Human-in-the-loop approval gates on anything irreversible or high-value
Memory and state management so an agent can resume long-running work
Multi-agent handoffs where a specialist agent takes over a subtask
Full execution traces showing every decision, tool call, and intermediate result

Deliverables

What ships with the engagement

Agent scope document defining goals, tools, and hard permission limits
Evaluation suite with recorded scenarios and expected outcomes
Trace viewer for inspecting any run step by step
Approval interface for the gated actions in your workflow
Cost and latency instrumentation per agent run

FAQ