As a product manager in the e-commerce space, I’m constantly monitoring how technology is reshaping buyer behavior, not just in what we buy, but how we decide to buy. My fascination starts with understanding human motivation. I often turn to Maslow’s hierarchy of needs as a mental model for commerce. When you start thinking about buying behavior through this lens: survival, safety, belonging, esteem, and self-actualization; you begin to see product categories aligning with these tiers.
Although not perfectly but approximately, groceries and hygiene align with physiological needs. Home security devices, childproofing speak to safety. Toys and gifts reflect belonging. Luxury fashion and personal electronics feed into esteem. And books, hobby kits, and learning tools push us toward self-actualization. These aren’t just product categories, they’re reflections of human drivers.
To ground this framework in real behavior, let’s look at how U.S. consumers spent across these need categories in 2024 (from ECDB):
- Physiological (survival): $88.3B (~7.4% of U.S. e-commerce): led by groceries, hygiene, and essentials.
- Safety (protection, stability): $99B (~8.3%): home security, health & wellness products.
- Belonging (family, community): $246.3B (~20.7%): toys, seasonal decor, pet care, shared gifting.
- Esteem (status, beauty, recognition): $256.7B (~21.4%): fashion, beauty, premium electronics.
- Self-Actualization (purpose, growth): $294.1B (~24.7%): books, learning tools, hobby kits.
- Mixed/Other: $208.2B (~17.5%): furniture, long-tail categories crossing needs.
These numbers show that the largest slices of e-commerce are no longer driven by need alone, but by emotional and aspirational intent. That insight shaped how I approached the agent's design. Now, as we step into a new era of interaction where AI agents and AR glasses are about to rewire the commerce funnel. Everything from discovery to purchase will most probably change.
The traditional funnel: discovery → add to cart → checkout: is no longer enough. As AI becomes more context-aware and capable, the buying journey is evolving into a richer, multi-stage experience:
- Intent Recognition – An agent picks up cues from your behavior, environment, or visual triggers before you even actively search.
- Discovery/Search – Visual input or contextual insight prompts a search or product match.
- Evaluation – The agent compares reviews, specs, and alternatives, personalized to your values.
- Selection (Carting) – Products are added to a dynamic cart that may span multiple platforms.
- Checkout & Fulfillment – Payment, delivery, and preference management happens in one flow.
- Post-Purchase Feedback Loop – Returns, reorders, gifting, or learning-based insights update future behavior.
We’re still early in this evolution. While we don’t have smart glasses natively supporting all these steps yet, we do have tools to build nearly everything else. My focus is on bridging that gap, building what we can today (vision recognition, agentic reasoning, cart/payment orchestration), so that we’re ready the moment the hardware catches up. In the traditional e-commerce funnel, we start with discovery or search, proceed to add to cart, and then complete checkout. But soon, we won’t need to initiate search at all.
AI agents will:
- Discover products through real-world context and image recognition (especially with smart glasses)
- Search and compare based on your budget, preferences, and purchase history
- Add to cart, even across multiple stores or platforms
- Handle checkout, payment, and even delivery preference, without friction
The infrastructure is being shaped now, so when smart glasses hit mass adoption, we’ll be prepared. Early signs are already here: Meta’s Ray-Ban smart glasses are integrating multimodal AI, Google Lens enables visual search from smartphones, and Apple’s Vision Pro hints at a spatial future where product discovery becomes visual and immersive. While full agentic integration with AR hardware isn’t yet mainstream, these innovations are laying the groundwork. We're positioning our agent infrastructure, vision grounding, reasoning, and checkout flows to plug into these platforms as they mature. As AR glasses evolve and LLMs get smarter, we're stepping into a world where shopping doesn’t start with a search bar it starts with sight. You look at a product. The agent sees it. It identifies, reasons, compares, and buys all in the background.
I made a serious attempt at visualizing this future and built a working prototype that explores the workflows needed to support visual discovery and agent-driven buying. The concept: an AI agent that takes visual input (like from smart glasses), identifies the product, understands your intent based on need, and orders it using the right marketplace (Amazon, Walmart, or even smaller verticals).
How It Works: A Quick Flow
This section outlines the user journey: how visual input from a smart glass becomes a completed e-commerce transaction, powered by layered AI agents.
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User looks at a product IRL (a sneaker, a couch, a protein bar)
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Smart glasses capture the image and pass it to the Visual Agent
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The agent does image-to-text grounding ("This looks like a Nike Air Max")
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Based on your current need state (inferred via Maslow-like tagging, past purchase, mood), it:
- Launches a LLM Search Agent to summarize product comparisons or
- Directly pings Amazon/Walmart/Etsy depending on context
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The best match is added to cart, or flagged as:
- Buy now
- Save for later
- Recommend alternative
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Optional: It syncs with your calendar, wardrobe, budget, household agents
The Stack Behind the Scenes
A breakdown of the technical architecture powering the agentic experience, from image recognition to marketplace integration.
- Smart Glass Visual Input: Captures image of the object in view
- Phi Agent + Groq LLaMA 3: Handles reasoning, dialogue, and multi-agent orchestration
- Image Recognition: CLIP + Segment Anything + MetaRay for grounding
- E-Commerce Scraper Tools: Custom tools for Amazon, Walmart, Etsy, Mercado Livre, etc.
- Maslow Need Engine: Classifies product into Physiological, Safety, Belonging, Esteem, or Self-Actualization
- Cart + Payment Agent: Interfaces with Stripe, Plaid, or store-specific checkout APIs
Need-Based Routing: From Vision to Marketplace
By tagging products against Maslow’s hierarchy of needs, the system decides which buying experience to trigger : instant order, curated review, or mood-matching suggestions.
We used our earlier Maslow mapping to dynamically decide how to fulfill a visual product intent:
- Physiological (e.g. food, hygiene) → Instant fulfillment via Amazon Fresh / Walmart Express
- Safety (e.g. baby monitor, vitamins) → Review summary via LLM before purchase
- Belonging (e.g. toys, home decor) → Pull family sentiment / wishlist context
- Esteem (e.g. fashion, beauty) → Match wardrobe, suggest brand alternatives
- Self-Actualization (e.g. books, hobby kits) → Check learning path, recommend add-ons
Real Example: The Coffee Mug
This simple use case shows the agent in action, recognizing a product visually and making a smart decision based on your behavior and preferences. Say for example, you’re at a friend’s place or even watching TV. You find an attractive coffee mug.
Your smart glasses:
- Identify it visually ("Yeti 14oz Travel Mug")
- Search Amazon, Walmart, and Etsy
- Check your preferences (already own 3 mugs? On a budget?)
- Suggests:
- "Buy from Amazon, ships in 2 days"
- "Cheaper variant on Walmart"
- "Match with home decor? Tap to see moodboard"
You blink twice. It adds to cart. Done.
Agent Collaboration in Action
No single model runs the show. This isn't one monolithic agent. It’s a team of agents working asynchronously:
1. Visual Agent — Image → Product Candidates
from phi.tools.vision import VisualRecognitionTool
class VisualAgent(VisualRecognitionTool):
def run(self, image_input):
# Use CLIP or MetaRay backend
return self.classify_image(image_input)
2. Need Classifier — Product → Maslow Tier
from phi.tools.base import Tool
class NeedClassifier(Tool):
def run(self, product_text):
# Simple rule-based or LLM-driven tagging
if "toothpaste" in product_text:
return "Physiological"
elif "security camera" in product_text:
return "Safety"
elif "gift" in product_text:
return "Belonging"
3. Search Agent — Query → Listings
from phi.tools.custom_tools import WebSearchTool, EcommerceScraperTool
class SearchAgent:
def __init__(self):
self.web = WebSearchTool()
self.ecom = EcommerceScraperTool()
def search(self, query):
return self.web.run(query) + self.ecom.run(query)
4. Cart Agent — Listings → Optimal Choice
class CartAgent:
def run(self, listings):
# Simple scoring based on reviews, price, shipping
ranked = sorted(listings, key=lambda x: x['score'], reverse=True)
return ranked[0] # Best item
5. Execution Agent — Product → Purchase
class ExecutionAgent:
def run(self, product):
# Placeholder: simulate checkout API
return f"Initiating checkout for {product['title']} via preferred vendor."
All in a few seconds ambient commerce, just like we imagine it.
What I Built (sample MVP Stack)
A snapshot of the real-world tools used to prototype this concept, combining LLMs, vision models, cloud infra, and front-end flows.
from phi.agent import Agent
from phi.model.groq import Groq
from phi.tools.custom_tools import WebSearchTool, EcommerceScraperTool
# Instantiate the AI agent
agent = Agent(
model=Groq(id="llama3-8b-8192"),
tools=[WebSearchTool(), EcommerceScraperTool()],
description="Agent that recognizes visual input and recommends best e-commerce options."
)
# Sample query to test visual-to-commerce agent workflow
agent.print_response(
"Find me this product: [insert image or product description here]. Search Amazon and Walmart and recommend based on price, delivery, and reviews.",
markdown=True,
stream=True
)
- Bolt.new (Vibe coding) for UI
- Supabase for user session storage + purchase history
- Netlify for deploy
- GroqCloud for fast inference
- phi.agent to orchestrate multi-tool logic
- CLIP + Playwright for image-to-product matching
Final Thought
This isn’t just about faster checkout. It’s about shifting the entire paradigm of commerce:
From: "I need to search for this thing"
To: "I saw something cool, and my AI already knows if it fits my life."
This is the future of buying: ambient, agentic, emotionally aware. If you're building for this world, let's connect.