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. Physiological (survival): $88.3B (~7.4% of U.S. e-commerce): led by groceries, hygiene, and essentials. Physiological (survival) Safety (protection, stability): $99B (~8.3%): home security, health & wellness products. Safety (protection, stability) Belonging (family, community): $246.3B (~20.7%): toys, seasonal decor, pet care, shared gifting. Belonging (family, community) Esteem (status, beauty, recognition): $256.7B (~21.4%): fashion, beauty, premium electronics. Esteem (status, beauty, recognition) Self-Actualization (purpose, growth): $294.1B (~24.7%): books, learning tools, hobby kits. Self-Actualization (purpose, growth) Mixed/Other: $208.2B (~17.5%): furniture, long-tail categories crossing needs. Mixed/Other 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. 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 Discover products through real-world context and image recognition (especially with smart glasses) Discover products Search and compare based on your budget, preferences, and purchase history Search and compare Add to cart, even across multiple stores or platforms Add to cart Handle checkout, payment, and even delivery preference, without friction Handle checkout 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. sight 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). 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 How It Works: A Quick Flow 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. User looks at a product IRL (a sneaker, a couch, a protein bar)


Smart glasses capture the image and pass it to the Visual Agent


The agent does image-to-text grounding ("This looks like a Nike Air Max")


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



The best match is added to cart, or flagged as:

Buy now
Save for later
Recommend alternative



Optional: It syncs with your calendar, wardrobe, budget, household agents User looks at a product IRL (a sneaker, a couch, a protein bar) User looks at a product IRL (a sneaker, a couch, a protein bar) Smart glasses capture the image and pass it to the Visual Agent Smart glasses capture the image and pass it to the Visual Agent The agent does image-to-text grounding ("This looks like a Nike Air Max") The agent does image-to-text grounding ("This looks like a Nike Air Max") 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 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 Launches a LLM Search Agent to summarize product comparisons or Directly pings Amazon/Walmart/Etsy depending on context The best match is added to cart, or flagged as:

Buy now
Save for later
Recommend alternative The best match is added to cart, or flagged as: Buy now
Save for later
Recommend alternative Buy now Save for later Recommend alternative Optional: It syncs with your calendar, wardrobe, budget, household agents Optional: It syncs with your calendar, wardrobe, budget, household agents The Stack Behind the Scenes 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 Smart Glass Visual Input: Captures image of the object in view Smart Glass Visual Input Phi Agent + Groq LLaMA 3: Handles reasoning, dialogue, and multi-agent orchestration Phi Agent + Groq LLaMA 3 Image Recognition: CLIP + Segment Anything + MetaRay for grounding Image Recognition E-Commerce Scraper Tools: Custom tools for Amazon, Walmart, Etsy, Mercado Livre, etc. E-Commerce Scraper Tools Maslow Need Engine: Classifies product into Physiological, Safety, Belonging, Esteem, or Self-Actualization Maslow Need Engine Cart + Payment Agent: Interfaces with Stripe, Plaid, or store-specific checkout APIs Cart + Payment Agent Need-Based Routing: From Vision to Marketplace 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: how 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 Physiological (e.g. food, hygiene) → Instant fulfillment via Amazon Fresh / Walmart Express Physiological (e.g. food, hygiene) Safety (e.g. baby monitor, vitamins) → Review summary via LLM before purchase Safety (e.g. baby monitor, vitamins) Belonging (e.g. toys, home decor) → Pull family sentiment / wishlist context Belonging (e.g. toys, home decor) Esteem (e.g. fashion, beauty) → Match wardrobe, suggest brand alternatives Esteem (e.g. fashion, beauty) Self-Actualization (e.g. books, hobby kits) → Check learning path, recommend add-ons Self-Actualization (e.g. books, hobby kits) Real Example: The Coffee Mug 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" 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" "Buy from Amazon, ships in 2 days"
"Cheaper variant on Walmart"
"Match with home decor? Tap to see moodboard" "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 Agent Collaboration in Action No single model runs the show. This isn't one monolithic agent. It’s a team of agents working asynchronously: team of agents 1. Visual Agent — Image → Product Candidates 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) 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 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" 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 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) 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 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 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 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." 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) 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
) 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 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 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.

The code in this story is for educational purposes. The readers are solely responsible for whatever they build with it.

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