Before you run Amazon analytics inside your AI agent, you need to think about setting up proper guardrails.
An AI agent like Claude can do wonders for time savings. But out of the box, LLMs are very generic. They don’t have context for how your specific business operates.
They start from: okay, how does the average brand on Amazon do this?
To maximize value from your AI agent, you need to create a document that lays out the guardrails for each of your products.
Brett Bohannon talks about this a little bit in our recent webinar. (Stream it now if you haven’t already.)
Basically, this means spelling out which products have high LTV and can therefore tolerate more advertising loss, and which need to have really low ACOS. And so on.
Of course, a good MCP connection, like Intentwise’s AI Gateway, will feed your AI agent custom expertise about the overall e-commerce business.
AI Gateway will tell Claude the difference between brand and non-brand terms, for instance, so your AI agent will already know that you have very different ACOS expectations for brand vs. non-brand terms.
But even with this embedded expertise, you’re still going to need to supply your AI agent with information specific to your business.
What kinds of workflows should you set up inside your AI agent?
With the backing of a MCP connection like Intentwise’s AI Gateway, you can automate all kinds of tasks for your AI agent.
We’ve written before about using your AI agent to run analytics, and in fact we’ll be hosting a webinar on this very topic on Wednesday.
AI agents can also streamline how you build decks and reports, either for your clients or your internal teams.
You can also take advertising actions with your AI agent, especially if you develop a connection to the Amazon Ads MCP.
Before you scale up your use of your AI agent, we recommend thinking clearly about what kinds of tasks you want to automate.
Break down all of your work processes into more minute workflows that your AI agent can automate.
You might use your AI agent to automate budget pacing, search term analysis, wasted spend checks, or instruct it to surface bid adjustment opportunities or weekly reporting.
Whatever route you choose, you should think about the work you do every day and every week, and identify tasks that can be automated.
Why you need to set up AI guardrails
The reality is that your AI agent isn’t always going to understand the specific goals and limitations you have for every product.
Your marketing tactics are always going to differ based on your product. You’ll probably have a lot more tolerance for advertising losses on a newly launched product, or on a product with a really high LTV.
By contrast, you’re probably going to need to be more conservative with ad spend on a backlist product that shoppers don’t often repurchase.
These are the kinds of data points you need to feed your AI agent.
When you’re setting up your AI agent, we recommend creating a .md file of instructions that you feed into Claude.
In that .md file, sketch out your priorities for each of your products.
Here are a few starting places:
Goal. What are you trying to optimize right now for this product? Are you prioritizing NTB shoppers?
Are you prioritizing your Share of Voice and visibility online? Are you focusing on profitability?
Margin. What is your profit margin on each product, and how willing are you to lose money on it?
Budget. List out your budget limits on a daily as well as monthly basis.
Pain thresholds. Basically, how much loss are you willing to tolerate for each of your products?
At what point does a certain ACOS, ROAS, CPC, or TACOS stop being acceptable, and you need to pull back on spend or pause a campaign?
As we know, some products have a high LTV, and it’s okay if your campaign isn’t ROAS-positive. Spell out where that’s true.
Seasonality. When is your product in peak season? How much do you expect sales to drop off when it falls out of season?
Your AI agent should be able to identify that a dip in sales isn’t the result of a poorly performing campaign, but rather just normal seasonality patterns.
