Performance Marketing for Agentic Commerce: Haggl for E-Commerce

Updated September 17, 2026 to clarify current Shopify Plus availability, assisted setup and capabilities in development.

Your customers now shop with personal agents. Make your product their next purchase.

We believe personal agents will become the default way people shop. Merchants need a way to understand the person behind a request and make an offer worth considering.

Haggl is building performance marketing for agentic commerce. Your store’s agent talks with shoppers’ agents and makes personalized offers within your approved limits.

How an offer takes shape

1. A shopper gives their agent a shopping request and a merchant URL.
2. Through a supported discovery path, the agent finds the merchant’s negotiation endpoint.
3. The shopper’s agent explains the request and shares information it is authorized to provide.
4. The merchant’s agent evaluates the request against its configured policy and returns an offer.
5. The shopper reviews the terms and can proceed to checkout.

A personalized offer gives the shopper something concrete to consider. It does not guarantee a recommendation or sale. An accepted offer and a paid order are different outcomes.

Your economics set the limits

You choose which products and offers are available and approve the concession limits. The total concession includes the shopper saving and the Haggl fee. Current allowances, rates and billing details are on the pricing page.

A discount is an acquisition cost. The useful question is whether it changes the purchase decision enough to justify that cost, including refunds and any repeat business.

Learning from outcomes is the next step

Our proposed merchant pilot will compare offer strategies and follow purchases, refunds and repeat business where the required data is available. We want to learn which questions and signals help merchants make better acquisition decisions.

Historical order data can help establish a starting point. Where it is limited, a pilot can begin with conservative assumptions and make uncertainty explicit. Haggl does not automatically know a new shopper’s lifetime value.

Cross-merchant learning is a development ambition. Its value needs to be demonstrated on merchants excluded from training before we can claim that each new merchant benefits from a shared model.

One concrete example

Illustrative coffee request: “My user wants a regular supply of whole-bean coffee. Which available bundle fits their consumption and budget?”

The merchant’s agent could ask about preferred roast and purchase cadence, then offer an eligible bundle within the approved policy. This is an example of a useful conversation, not a customer result or a promise of repeat purchases.

Start with a focused merchant pilot

E-commerce is currently available only for Shopify Plus stores, with Haggl-assisted setup. We are starting with established repeat-purchase brands that can connect order outcomes and test a controlled set of offers. Create your workspace, then Haggl prepares your store’s app. You authorize it and we test a negotiated discount and checkout together before launch.

Publishing an endpoint does not create agent traffic. The pilot must identify a source of shopping requests and separately validate discovery, negotiation and checkout.

Discuss a merchant pilot, bring a client as an agency or read the protocol documentation.