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Agentic Shopping is an AI-driven shopping model where intelligent systems proactively
discover, curate, and personalize products for users without requiring manual search.
Instead of browsing catalogs, users receive coordinated product collections aligned
with their context, location, and preferences. Glance pioneered this category using a
multi-agent AI system that generates styled looks based on real-time signals.

What is Agentic Shopping?

Traditional online shopping depends on search bars, filters, and manual browsing. Users
must explore large catalogs before finding something relevant.
Agentic Shopping works differently. AI interprets signals such as context, behavior, and
identity to generate curated collections automatically.
The key shift is:
Traditional ecommerce → search driven
Agentic shopping → AI driven discovery
In Glance, styled collections are generated in real time using signals such as weather,
trends, and personal attributes.

How is Agentic Shopping different from traditional online shopping?

A true agentic shopping system goes beyond recommending individual products. It
understands context and intent before generating results.
Key capabilities include:
• Understanding contextual signals such as weather and events
• Interpreting identity signals like physical attributes or style preferences
• Generating complete styled looks rather than isolated products
• Continuously improving through behavioral learning
These capabilities allow the system to act more like a personal shopping assistant than
a product search engine

What defines a true agentic shopping system?

Predictive shopping helps reduce the effort involved in finding relevant products.
Instead of presenting thousands of options, AI systems narrow choices using signals
such as behavior, context, and timing. This reduces decision fatigue and helps users
discover items that fit their current needs.
In Glance, prediction is powered by behavioral signals collected across hundreds of
millions of devices, allowing the system to anticipate what users are most likely to
engage with. That data depth is what separates genuine prediction from basic filtering.

Why is predictive shopping becoming important?

No. Search remains useful when users already know what they are looking for.
Agentic Shopping complements search by presenting relevant discoveries before a
query is made. It focuses on inspiration and discovery rather than direct lookup.
In Glance, styled collections appear proactively so users can explore ideas before
deciding what to buy.

Does Agentic Shopping replace search completely?

Multi-agent AI architecture is a system design where multiple specialized AI agents
perform different tasks under a central coordinator. Instead of one model doing
everything, each agent focuses on a specific intelligence layer.
For example, separate agents may analyze:
• Weather and location
• Fashion trends
• Occasions or events
• Physical attributes
• Lifestyle signals
The coordinator combines these insights to generate structured outputs such as
complete styled looks.

What is multi-agent AI architecture?

Glance uses specialized agents that each focus on a different signal. One agent
analyzes weather and location. Another tracks fashion trends and seasonal shifts.
Another identifies occasions or lifestyle cues. Another evaluates physical attributes
from a selfie.
These agents work together under a central orchestration layer to generate cohesive
styled collections tailored to the user.

How does Glance use multi-agent intelligence?

Single model systems attempt to interpret many signals at once. Multi agent systems
allow specialization. Each agent focuses on a specific domain such as trends, context,
or physical attributes.
Because these agents collaborate, the final output reflects a deeper understanding of
the user’s situation. The result is a coordinated outfit rather than a list of unrelated
product recommendations.

Why does multi-agent design create better outcomes than single-model systems?

Most recommendation systems rely on past shopping history to personalize results. This
creates what is known as the “cold start problem” for new users.
Glance addresses this by using identity and contextual signals instead of purchase
history. A selfie helps infer physical attributes, location provides climate context, and
trend intelligence adds regional relevance.
Because personalization starts with these signals, Glance can generate styled looks
from the very first interaction.

How does Glance handle personalization without prior shopping history?

A selfie helps Glance understand physical attributes such as skin tone, face shape, hair
color, and body proportions. These signals help guide color combinations, silhouettes,
and accessories that may suit the user.
Glance also generates styled looks using the user’s own image so the outfits appear on
the user rather than a generic model. This helps users visualize how a look may work for
them.

How does Glance use a selfie to improve personalization?

Glance generates outfits as structured compositions rather than collections of
individual products. The system evaluates color harmony, silhouette balance,
proportion alignment, and trend compatibility before finalizing a look.
Each item is selected in relation to the others so the entire outfit works together
visually.

How does Glance ensure aesthetic coherence across an outfit?

Glance considers physical attributes, silhouette balance, and contextual needs when
generating styled looks. Over time, interaction patterns and return data help refine size
and fit suggestions.
The goal is to improve confidence in the selection before a purchase decision is made.

Can Glance improve fit accuracy?

Glance interprets behavioral signals such as swipe patterns, browsing speed,
interaction depth, and engagement sequence. These signals help the system
understand what users are interested in without requiring manual preferences.
Because the system learns from patterns across hundreds of millions of devices, it can
refine recommendations quickly.

How does Glance understand what I want without constant input?

Real-time context refers to situational signals that influence what products are most
relevant at a given moment.
These signals include:
• Current weather
• Regional fashion trends
• Seasonal timing
• Upcoming events or occasions
AI systems use these signals to adjust recommendations dynamically. For example, the
same user may see completely different outfit collections in summer versus winter or in
different cities.
Platforms like Glance treat context as a primary ranking signal, which is why
recommendations feel situationally relevant rather than generic.

How does real-time context influence recommendations?

Glance combines behavioral intelligence with a multi agent system designed
specifically for fashion discovery. The platform learns from signals such as engagement
patterns, dwell time, and browsing sequences across hundreds of millions of devices.
These signals continuously refine the agents responsible for personalization, styling,
and contextual matching.

What makes Glance’s AI different from other shopping tools? 

Yes. Glance is free to use and does not require a subscription. On compatible devices it
comes pre installed, which means many users already have access without
downloading a separate app.
This distribution model allows Glance to reach users before they begin searching for
fashion apps.

Is Glance free to use?

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