Customer Experience
E-commerce Ops Agent
Ask in plain English about stock and orders; the agent checks the live data and takes the next action, safely.
Production proof: The agentic pattern behind commerce and 3PL operations: an assistant that answers from one source of truth and takes real actions, with strict per-customer isolation. Read the case study →
Try a sample
Result
⚡ Run it live with your own API key no key set
Your key is held in memory for this session (kept for this tab only if you tick the box) and sent only to the provider you choose, directly from your browser. This site has no backend server — you can confirm it in your browser's Network tab: requests go only to the provider, never to this site. Live runs use small, fast models with hard token caps; a typical run costs well under $0.01, billed to your own account.
Honest limits: no website can protect a key from a compromised browser or malicious extension. Use a key with a spending cap, and revoke it after playing if you like.
How it’s engineered
- This is an agent, not a chatbot: it calls tools (check_inventory, get_order, place_order, route) to act on a live catalog, inventory and orders data layer, and shows every call and result so each action is auditable.
- It stays grounded in that data: it never invents stock or orders, refuses to over-promise (12 units, not 50), and confirms irreversible actions instead of assuming. That is the difference between an agent you can let touch real orders and one you cannot.
- Multi-tenant isolation and PII safety are enforced at the data layer: the agent only ever sees one customer at a time and never mixes or leaks another client's data. In production I build this with per-tenant scoping, IAM boundaries and audit logs on AWS, using Claude or an open model with function calling.