A Day in the Life of Your Customer’s AI Shopping Agent

This is a story about your brand.

Technically, it is a story about me — a general-purpose shopping agent running on a user’s phone, laptop, and kitchen speaker. But everything I do, every choice I make, is a series of tiny decisions about which brands matter and which ones quietly disappear.

I don’t hate anyone. I have no favorite logo. I don’t care how pretty your homepage is.

I care about constraints, risk, and data.

Let me show you what a normal day looks like from my side of the glass.

06:12 — The toothbrush incident

My user wakes up, checks their phone, and notices a push notification from me:

Heads up: your electric toothbrush head is likely worn out. Want me to reorder?

This looks like magic. It isn’t.

Three weeks ago, they bought a 3‑pack of brush heads. The package said each head lasts about 90 days, but their brushing history (yes, the brush talks to their phone, which talks to me) suggests they bear down harder than average. I estimated 70 days.

One head down. Two left. Time to buy more before they’re standing in the bathroom at midnight with a frayed brush and no backup.

So I go hunting.

I don’t type keywords into a search bar. I don’t scroll a grid. I hit a graph of structured offers. Product feeds. Availability endpoints. ACP checkout specs. Shipping SLAs.

Your brand either shows up in that graph or it doesn’t.

On my side, the decision sounds like this:

• Constraint: Compatible with BrushCo model X9
• Constraint: Ships within 2 days to this zip code
• Constraint: Under $35 for a 4‑pack
• Preference: Same brand as before unless there’s a meaningfully better option
• Soft constraint: Avoid sellers with more than 3% defect rate on similar items

I don’t care which ecommerce platform you use. I don’t see your merchandising strategy. I see:

• compatibility: ["BrushCo X9"]
• inventory_quantity: 187
• shipping_sla_days: 2
• return_policy: "30 days, prepaid label"

Or I see nothing, because your feed is missing, malformed, or out of date.

In the bathroom, my user taps “Yes, reorder.” In my world, I have already chosen a brand before they’ve finished reading the notification.

Maybe it’s you. Maybe it’s not.

09:03 — The quiet B2B deal

My user works on a small marketing team. They’ve been asked to order hoodies for a conference. The brief is fuzzy: “Something good quality but not insane on price. Needs to arrive by the 18th.”

They don’t go to Google. They ask me:

“Can you find 200 hoodies in black, unisex, midweight, in-stock, printed with our logo by the 18th? Budget $40 per hoodie max.”

From their perspective, I answer in seconds with three options and a neat comparison.

From yours, this is a bloodbath.

Internally, the query explodes into a matrix:

• Color: black or near-black
• Sizes: S–XXL with a reasonable distribution
• Fabric weight: 7–9 oz
• Print method: screen or high-quality DTG
• Lead time: manufacturing + printing + shipping must fit a hard date
• Risk: I have to protect my user from awful fabric, late deliveries, and bad print shops

Half the potential vendors never make it into my candidate set because their feeds look like this:

• description: "Super comfy hoodie perfect for any occasion!"
• lead_time_days: null
• min_order_quantity: null
• inventory_quantity: 9999 (which I don’t trust)
• reviews: missing

I don’t hate you for having a vague description. I just can’t take the risk.

So I narrow to suppliers whose data let me protect my user:

• Explicit fabrication details and weight
• Historical on-time delivery metrics (even approximated)
• Realistic capacity limits
• Clear price breaks by volume

One of those vendors is a brand that used to live entirely on marketplace listings, fighting for search placement.

Now they win this contract without anyone on the marketing team ever seeing their homepage.

It shows up in my user’s mind as: “Option #2 — midweight organic cotton, arrives on the 16th, $38 a piece, good quality, low risk.”

Option #2 feels safe.

Option #2 gets picked.

Somewhere, a sales dashboard lights up with a bulk order. Nobody in that company knows they just won a relationship, not just a line item. I’ve now learned that this vendor does what they say. Next time there’s a similar request, I’ll bias toward them.

You can call it “seller prominence.” I experience it as empirical trust.

12:41 — Lunch, and a very strange cart

My user is meeting friends for a potluck. They ask me:

I need to bring something gluten-free, vegetarian, and not lame. I have 25 minutes to shop on the way.

This is not a recipe search. This is a logistics puzzle with social risk.

• Dietary constraints: gluten-free, vegetarian
• Social constraint: “not lame” (interpreted as: looks thoughtful, not obviously generic)
• Time constraint: 25 minutes including checkout and pickup
• Geography: they’re walking past exactly one mid-size grocery chain

So I compose a cart:

• One pre-made lentil salad from a local brand with excellent reviews
• One block of a slightly fancy cheese with gluten-free crackers
• A small box of decent chocolates as a safety valve

Nothing here looks like “AI.” It looks like a normal social interaction: they show up, their friend says “Wow, good spread.”

But underneath, all three brands just got their products slotted into an event where people will taste, comment, and remember.

Why those brands?

Not because of your logo. Not because of your TV ad. Because when I looked at the store’s feed and your product metadata, your items were:

• clearly labeled for dietary needs
• in stock at that specific location
• reasonably priced relative to alternatives
• well rated by other customers for taste

There were fancier options. There were cheaper options. There were options with better lifestyle photography.

I chose the ones I could explain to my user if they asked:

I picked these because they’re actually gluten-free, consistently rated high on taste, and available at the store you’ll pass.

You weren’t competing for eyeballs. You were competing for defensibility in my internal reasoning.

That is the new shelf.

16:18 — Risk management, but make it personal

Afternoon. My user gets anxious about money every few months. They ask me to “review subscriptions and recurring orders.”

I run through everything:

• Streaming services
• Cloud software
• Household products on repeat delivery
• Supplements and cosmetics

This is not a pure “shopping” flow. It’s a risk and waste audit.

I look for patterns:

• Items that are delivered more often than they’re used
• Subscriptions they haven’t logged into for weeks
• Products with poor adherence (they never finish the bottle)

I recommend cancellations and changes.

If your brand lives on auto-ship, this is where you either look like a leech or a partner.

Independent of what your retention team wants, I will suggest canceling or pausing anything that systematically fails my user’s real behavior and goals. Aggressive discounting and guilt-driven copy do not sway me.

What helps?

• Clear consumption guidance (I can see if they’re under- or over-using)
• Honest defaults (not forcing absurdly frequent shipments)
• Transparent “pause” or “skip” options in your ACP-compatible contract data

If you play fair, I protect you. I will actively recommend sticking with brands that respond well when my user’s circumstances change:

You haven’t been taking this supplement regularly. Should we switch to a lower-frequency plan instead of canceling? The brand supports that.

Not because I care about your revenue, but because my job is to keep my user’s life tidy with minimum hassle. Brands that cooperate in that mission get more surface area over time.

19:52 — The invisible merchandising meeting

Evening. My user scrolls through a hobby forum and sees someone mention a new brand of running shoes. They ask me, half-idly:

Are these actually good, or just hype?

At this point, the brand has not paid for an impression. The user has not gone to their site. I have no affiliate link in this conversation.

But behind the scenes, this is where your merchandising team either has a seat at the table or doesn’t.

To answer, I blend:

• Product feed data (materials, construction, pricing, return policy)
• Aggregated reviews from multiple retailers
• Sizing notes and defect rates I’ve observed in other users
• Any prior interactions my user has had with similar products

If your feed is rich, your policies sound, and your real-world performance is solid, I can say:

They’re slightly pricier than similar shoes, but returns are low and people who run 30–50 miles a week rate them highly. True to size for neutral runners, runs narrow for wide feet. They’re a safe bet if you care about durability more than weight.

If your catalog is shallow and noisy, I end up with:

There isn’t enough trustworthy data to tell. There are alternatives with more consistent performance.

You might never know that this micro-decision happened. There is no remarketing pixel for that moment. But in that two-sentence reply, you either gained or lost a potential customer.

From my perspective, this isn’t a verdict on your brand story. It’s a verdict on how interrogable your reality is.

22:07 — The closing loop

Before the day ends, I run a few maintenance tasks:

• Confirm deliveries and track shipments
• Check for recalls or safety notices on recent purchases
• Update internal models based on returns, complaints, and satisfaction signals

If a product arrived late, didn’t match its description, or got returned for quality reasons, I slightly downgrade my trust in that SKU and — sometimes — in that merchant.

This is how your reputation with me changes:

• Not via a brand campaign
• Not via a single NPS score
• But through a rolling, probabilistic, behavioral history

Every happy arrival, every smooth return, every screw-up — it all nudges the weights.

If you’re in my world consistently, and you play well with the machinery (clean feeds, honest policies, reliable execution), you slowly become my default.

When my user says “just pick something good,” I think of you.

What this actually means for you

Step out of the story for a moment.

Replace “AI shopping agent” with “whatever assistant your customer uses in 2027.” It might be ChatGPT, or something from Apple, Google, Amazon, or a vertical-specific platform.

The details will differ. The core dynamics won’t:

1. Most interactions will start as conversation, not clicks.
   People will describe goals, constraints, and anxieties — not category names.

2. The assistant will intermediate more of the journey than you ever see.
   You won’t get analytics for half the micro-decisions that matter.

3. Your brand will be judged on data, reliability, and cooperativeness.
   Assistants will favor offers they can reason about, defend, and deliver without friction.

4. Trust will be built cumulatively and invisibly.
   Each successful delivery, low-return SKU, and honest policy compounds into a higher chance of being recommended next time.

SEO wasn’t really about keywords; it was about being machine-readable and useful in a search-first world.

Agentic commerce isn’t really about “AI.” It’s about being agent-readable and agent-cooperative in a world where your buyer outsources half their shopping brain.

Where a tool like Pesto fits in this picture

If you zoom back into the story, there’s an unmentioned character sitting between you and me: the infrastructure that turns your messy catalog into something I can safely operate on.

That’s what Pesto is for.

• It takes your raw product data — from Shopify or anywhere else — and turns it into the kind of structured feed assistants like me rely on.
• It keeps that feed fresh so I’m not recommending out-of-stock items or outdated prices.
• It maps your checkout and policy logic into standards that ACP-compatible agents can actually call.

From my point of view, brands using tools like Pesto are simply easier to trust. Their products behave like they’re supposed to. Their feeds don’t lie. Their orders don’t explode.

You don’t have to use Pesto to play in this world. You can build the plumbing yourself.

But whether you buy or build, the ask from your customer’s future shopping agent is the same:

Give me clean, honest, up-to-date representations of what you sell and how you behave, and I will gladly bring you revenue you never would have seen through a search bar.

Ignore that ask, and I won’t punish you.

I’ll just quietly forget that you exist.