Retail and digital commerce are entering a phase where interfaces stop asking for clicks and start interpreting intent.

In this reality, using only one standard user experience (UX), where everyone follows the same steps or sees the same design, is no longer sufficient.

Experiences must become unique, proactive, and invisible: unique to each person, proactive in anticipating their needs, and invisible so that help is offered exactly at the right moment and place, often without the user even having to ask. This requires orchestrating data signals, agent frameworks, and intent-led design so the front end of any channel can quickly adapt in real time.

The urgency has intensified with the rise of AI shopping agents. Product discovery now begins within AI assistants, and, in many cases, the entire purchase journey concludes there, through in-chat checkout.

Engagement data reveals that sessions referred by AI agents arrive much closer to a buying decision and exhibit distinct behaviors compared to traditional traffic. This shift makes AI-optimized content and well-structured product data not just important but mission-critical.

Organizations must therefore plan for two fronts simultaneously: 1) meet customers inside third-party agent ecosystems, and 2) launch their own agents that carry brand identity and domain expertise.

Top five considerations for building an exceptional agentic commerce experience

1. Design an agent experience that reflects your brand’s values

Why this matters: In agentic commerce, the agent itself is the experience. It represents your brand, interprets intent, and decides how to present choices, so make it count. If you don’t design it deliberately, you’ll inherit a generic presence shaped by external platforms.

Essential steps to take:

  • Define the agent: Choose its visual presence and interaction mode (chat, voice, multimodal). Align its tone and vocabulary with your brand personality (e.g., expert, friendly, concise).
  • Codify language and behaviors: Decide how it explains recommendations, compares alternatives, and summarizes trade-offs. Make “why this?” a standard part of its dialogue to build trust.
  • Ensure catalog fluency: Teach the agent your taxonomy, attributes, bundles, inventory, and promotions so it curates relevant options at the moment of intent.
  • Facilitate learning: Capture signals about relevance, confusion, and friction so the agent can continuously refine its guidance.

Result: Your agent will be a credible, helpful concierge rather than a generic chatbot.

Remember, your agent is now the new front door to your brand.

2. Turn data into real-time context

Why this matters: Agents thrive on current signals – who the user is, where they are, what inventory and prices look like, and how user behavior is unfolding now. Static data produces generic output; activated signals produce contextual magic.

Essential steps to take:

  • Establish a unified signal layer integrating identity, inventory, pricing, and behavioral data into one trusted source of truth.
  • Move from SEO to GXO (generative experience optimization). Structure product content and media so generative systems can ingest, cite, and rank your information across assistant surfaces.
  • Blend trust signals (ratings, guarantees, certifications) into recommendations. Agents should surface credibility alongside utility.

Result: Journeys adapt moment by moment, enabling proactive nudges (e.g., “in stock nearby,” “bundle saves $X,” “ships by tomorrow”).

3. Build adaptive, multimodal UX (no two users or agents see the same thing)

Why this matters: Rigid templates assume a single pathway. Adaptive UX assembles components dynamically based on intent state, device, and context, and renders a consistent experience for customers and agents.

Essential steps to take:

  • Create dynamic component libraries for conversational, voice, and visual interfaces – and make them state-aware (e.g., ready to buy versus still exploring).
  • Synchronize product data and media across channels so assistants and human UIs present the same truth.
  • Expose machine-readable metadata (taxonomy, specifications, availability, price cues) to ensure assistants can see and assemble your experience accurately.

Result: Fewer steps to complete, higher intent capture, and consistent outcomes whether the journey begins on your site or in a third-party assistant.

4. Orchestrate a multi-agent architecture (specialized agents coordinated by a conductor)

Why this matters: Modern commerce no longer follows a linear flow; instead, a network of specialized agents (service, commerce, knowledge) is operating across your stack and partner platforms. Without a conductor, interactions fragment and value leaks.

Essential steps to take:

  • Implement an orchestration layer that coordinates agent roles, APIs, workflows, and handoffs.
  • Deploy in-house agents that leverage proprietary data to deliver distinctive guidance that hedges against total reliance on external ecosystems.
  • Define escalation and handoff protocols between AI and human agents for sensitive or complex scenarios.

Result: This creates a resilient, scalable fabric for intent interpretation, decision-making, and fulfillment across channels and partners.

5. Embed trust, transparency, and governance (invisible AI still needs visible guardrails)

Why this matters: When systems act on behalf of users, trust becomes the profit driver. Shoppers accept invisible AI as long as data use is clear, benefit is tangible, and humans remain available when stakes are high and risks are involved.

Essential steps to take:

  • Make consent visible and simple, explain what data is collected and why, and make it easy for users to opt out.
  • Standardize interpretability and transparency – teach your agent to explain why it recommended a product, and what factors were taken into consideration.
  • Design the system with a human-in-the-loop approach, clearly defining escalation paths and specifying availability windows.
  • Audit for bias, safety, and compliance, and integrate responsible AI reviews into release cycles. Trust isn’t a banner; it’s an operating standard embedded into agent behavior and front-end design.

Measuring the impact and success of AI agents

Keep traditional outcomes like conversion lift, larger baskets, lower abandonment, empowered human agents, and unified channels front and center. But don’t forget to add the equally important agent metrics:

  • Share of demand originating from assistants (how much of it starts off-site?)
  • Speed of intent to checkout for agent-referred sessions (do agentic users move faster?)
  • GXO visibility across leading AI models (are assistants citing and recommending you?)

Digital’s appeal has always been its high reward for low effort. Agentic commerce amplifies that promise provided we design the agent, activate signals, adapt the front end, orchestrate specialists, and govern for trust.

Organizations that treat assistants as first-class surfaces while shipping their own distinctive agents will earn relevance in a marketplace where intent is interpreted before a click is ever made.

Your customers are already interacting with AI shopping agents – are your experiences designed to meet them there?

Contact us to begin shaping an adaptive UX that’s agent-ready for your brand.