On most eCommerce teams, data isn’t missing. There’s too much of it, and it lives in silos: the retailer portal, the marketplace dashboard, the store platform, the ads account and dozens of spreadsheets. The problem is time: teams spend hours processing data by hand to reach a conclusion, and by the time they decide, the market has already moved.
AI agents change that equation. They don’t replace the team. They do the work of reading, comparing and prioritizing every day, and bring each person the concrete action along with the data that justifies it. The shift is already showing up in sales data: Salesforce estimates that AI and agents influenced about $67 billion in global Cyber Week 2025 sales (Salesforce, December 2025).
These are the 6 agents we recommend for a consumer brand, in the order they’re best implemented.
1. Shelf and availability agent
What it does: checks stock, search ranking and Buy Box for your priority SKUs at every retailer and marketplace, every day.
What it tells you: “SKU X has been out of stock for 14 hours at retailer Y; yesterday it sold Z units a day. Owner: Y’s KAM.”
Data it needs: priority SKU list, retailers and the digital shelf capture (in-house or from a tool like PULSO).
2. Price and promotion agent
What it does: compares your prices with MSRP and with your direct competitors, and detects competitor promotions.
What it tells you: “Your main competitor cut prices 12% on 3 category SKUs at two retailers since Tuesday. Your Share of Shelf on those searches dropped 8 points.”
Data it needs: your own and competitor prices by retailer, pricing policy and promotional calendar.
3. Content and listings agent
What it does: audits the completeness and quality of your product listings (title, images, attributes, description) and reads new reviews.
What it tells you: “Four listings on marketplace X have less than 60% of attributes filled in. There are 11 new reviews mentioning packaging; 7 are negative.”
Data it needs: published listings, your content standard and reviews.
4. Marketplaces and brand protection agent
What it does: detects new sellers on your SKUs, below-MSRP pricing and signs of questionable product.
What it tells you: “An unauthorized seller appeared on SKU X priced 22% below MSRP; it held the Buy Box 60% of yesterday.”
Data it needs: authorized distributor list, minimum prices and the listing capture by SKU.
5. Own store agent
What it does: reviews conversion, speed, checkout errors, zero-result on-site searches and abandonment every day.
What it tells you: “Mobile conversion dropped 18% since Thursday’s deploy. The error is concentrated in the shipping step.”
Data it needs: site analytics, error logs and the store platform.
6. AI and AI Shelf agent
What it does: measures every week how AI assistants (ChatGPT, Gemini, Perplexity) recommend you versus your competitors, which sources they cite and where they send people to buy.
What it tells you: “Out of 30 shopping questions in your category, you show up in 9; your competitor in 17. The most cited sources are two comparison sites and the listings on a marketplace where your content is incomplete.”
Data it needs: the shopping question set and the competitor list. We explain it in AI Shelf: what it is and how to measure it.
How they work together
The six agents feed an orchestrator that does what a good eCommerce director would do every morning: gather the findings, remove duplicates, prioritize by impact and send each owner a short list. A few actions, each with its data, its owner and its estimated impact.
The rule that matters most: agents propose and a person approves. In our model, the account lead reviews the instructions before they reach the client’s team. Trust is built with good calls, not with promises.
How to start in 30 days
| Week | What happens |
|---|---|
| 1 | Pick 1 or 2 agents based on the most expensive pain point (usually shelf and price). Define SKUs, retailers and competitors. |
| 2 | Connect the data sources and calibrate thresholds (what is an alert and what isn’t). |
| 3 | Run in parallel: the agent proposes and the team compares against what it would have done. |
| 4 | Fine-tune, assign owners and move to daily operation. |
After that, add one agent a month. Within a quarter, the team has all six running.
Why Claude
We build the agents with Claude, by Anthropic. We chose that model for its ability to work with long documents and data, reason about business rules and explain why it recommends something. Balloon has been a certified Anthropic Claude Partner since June 2026.
In short
AI agents aren’t an innovation project. They’re how your team stops spending time processing data and starts spending it deciding. Start with one, the most expensive pain point, and add one a month.
If you want to see what the first agent would look like on your own data, we build it as part of the assessment. Request it here.
FAQ
What's the difference between an AI agent and a dashboard?
A dashboard shows data and waits for someone to look at it. An agent reads the data, detects what changed, prioritizes it and proposes a concrete action to the person responsible, with the data that justifies it.
Do the agents act on their own?
We recommend starting with agents that propose and a person who approves. As the team comes to trust the recommendations, some low-risk actions can be automated.
Which AI model do you use?
At Balloon we build agents with Claude, by Anthropic. Balloon is a certified Anthropic Claude Partner.